A Project Report on

SKIN DISEASE DETECTION USING

CONVOLUTION AND NEURAL NETWORKS
Submitted in partial fulfilment of the Requirements for the award of the degree of
BACHELOR OF TECHNOLOGY
in
ELECTRONICS AND COMMUNICATION ENGINEERING
by

P. Devika (O190481)
V. Ganesh (O190888)
K. Abhisimha (O190865)
P.Deepika (O190341)
P.Mounika (O190293)
V.Jyothi (O190757)

Under the Esteemed Guidance of

Mr. G. MALAKONDAIAH M.Tech

(Assistant Professor)

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

RAJIV GANDHI UNIVERSITY OF KNOWLEDGE TECHNOLOGIES
ONGOLE CAMPUS
2024-25

i
 DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

RAJIV GANDHI UNIVERSITY OF KNOWLEDGE TECHNOLOGIES

ONGOLE CAMPUS

CERTIFICATE

This is to certify that report entitled “ Skin Disease Detection Using Convolution and Neural
Networks” being submitted by P. Devika, V. Ganesh, K. Abhisimha, P.Deepika
P. Mounika, V.Jyothi bearing ID numbers O190481, O190888, O190865, O190341, O190293 and
O190757 respectively in partial fulfillment of the requirements for the award of the Bachelor of
Technology in Electronics and Communication Engineering to the Rajiv Gandhi University of
Knowledge Technologies, Ongole is a record of Bonafide work carried by them under my guidance
and supervision.

Mr G. Malakondaiah M.Tech., Mrs. N. Padmavathi.,

Assistant Professor, Head of Department,

Department of ECE, Department of ECE,

RGUKT Ongole. RGUKT Ongole.

ii
 APPROVAL SHEET

This project report entitled “ Skin Disease Detection Using Convolution and Neural Networks
” by P. Devika, V. Ganesh, K. Abhisimha, P.Deepika, P. Mounika, V.Jyothi bearing ID numbers
O190481, O190888, O190865, O190341, O190293 and O190757 respectively is approved for the
degree of Bachelor of Technology in Electronics and Communication Engineering.

Examiner(s) ____________________________

____________________________

Supervisors ____________________________

____________________________

Chairman ____________________________

Date: ________________________

Place: ________________________

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 DECLARATION

I declare that this written submission represents my ideas in my own words and where others'
ideas or words have been included, I have adequately cited and referenced the original sources. I also
declare that I have adhered to all principles of academic honesty and integrity and have not
misrepresented or fabricated or falsified any idea/data/fact/source in my submission. I understand that
any violation of the above will be cause for disciplinary action by the Institute and can also evoke
penal action from the sources which have thus not been properly cited or from whom proper
permission has not been taken when needed.

Signature

P. Devika (O190481)

V. Ganesh (O190888)

K. Abhisimha (O190865)

P.Deepika (O190341)

P.Mounika (O190293)

V.Jyothi (O190757)

Date: ____________________________

iv
 ACKNOWLEDGEMENT

It is my privilege and pleasure to express a profound sense of respect, gratitude and

indebtedness to my guide Mr G. Malakondaiah, Assistant Professor, Dept. of Electronics and
Communication Engineering, Rajiv Gandhi University of Knowledge Technologies, for her
indefatigable inspiration, guidance, cogent discussion, constructive criticisms and encouragement
throughout this dissertation work.

I express my sincere gratitude to Mrs. N. Padmavathi, Assistant Professor & Head of the
Department of Electronics and Communication Engineering, Rajiv Gandhi University of
Knowledge Technologies, for his suggestions, motivations and co-operation for the, successful
completion of the work. I extend my sincere thanks to Dr. Bhaskar Patel sir, Director,
Rajiv Gandhi University of Knowledge Technologies, Ongole for his encouragement. And also I thank
each individual of the RGUKT, Ongole campus for their impeccable support for my the internship.

With Sincere Regards,

P. Devika (O190481)

V. Ganesh (O190888)

K. Abhisimha (O190865)

P.Deepika (O190341)

P.Mounika (O190293)

V.Jyothi (O190757)

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 TABLE OF CONTENTS
PAGE NO:
CERTIFICATE ii
DECLARATION iii
APPROVAL SHEET iv

ACKNOWLEDGEMENT v

ABSTRACT vi

CHAPTER -1 INTRODUCTION
1.1 BACKGROUND AND MOTIVATION 1
1.2 CONTRIBUTION 2
1.3 PROBLEM STATEMENT 2
1.4 DRAWBACK OF EXISTING SYSTEM 3
1.5 PROPOSED SYSTEM 3
1.6 ADVANTAGES 4

CHAPTER -2 EXISTING SYSTEM 5

CHAPTER -3 LITERATURE SURVEY 6
CHAPTER -4 REQUIREMENT SPECIFICATION
4.1 SYSTEM REQUIREMENT ANALYSIS 11
4.2 FUNCTIONAL REQUIREMENT 11
4.3 NON-FUNCTIONAL REQUIREMENT 11
4.4 TOOLS AND TECHNOLOGY DETAILS 13

CHAPTER -5 SYSTEM ANAYLSIS

5.1 SYSTEM DESIGN 14
5.2 PRE POCESSING 14
5.3 ALGORITHMS USED IN FEATURE EXTRACTION & SVM 16

CHAPTER -6 SYSTEM DESIGN

6.1 DATA FLOW DIAGRAM 21
6.2 ACTIVITY DIAGRAM 22
6.3 USE CASE DIAGRAM 23

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 6.4 SEQUENCE DIAGRAM 23

6.5 COMMUNICATION DIAGRAM 24

CHAPTER -7 IMPLEMENTATION & RESULS

7.1 METHODOLOGY 25

7.2 SAMPLE CODE INDEX.HTML 29

7.3 ACCEPTING TESTING 36

7.4 SCREENSHOTS 36

CHAPTER -8 CONCLUSION AND FUTURE WORK 40

REFERENCES 41

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LIST OF FIGURES
S.NO NAME OF THE FIGURE PAGE NO.

1 Fig 1.1: Proposed System 3

2 Fig 4.3. I Non-functional requirements 12

3 Fig 5.1.1 data flow of proposed system 14

4 FIG 5.3.1 HOG algorithm 17

5 Fig 5.3. 2 Support Vector Machine 18
6 Fig 6.1.1 Data Flow 21

7 6.2.1 Activity Diagram 22

9 Fig. 6.3.Use Case Diagram 23

10 Fig. 6.4. Sequence Diagram 24

11 FIG 6.5. Communication Diagram 24

12 Fig 7.1.1: Methadology 25

13 Fig.7.1.2:Architecture Proposed System 26

14 Fig. 7.1.3 Dataset 27

15 Fig 7.4.1 interface 36

16 Fig 7.4.2 Sign In Page 37

17 Fig 7.4.3 Opening Page 37

18 Fig 7.4.4 Skin Disease Type 1 38

19 Fig 7.4.5 Skin Disease Type 2 38

20 Fig 7.4.6 Skin Disease Type 3 39

viii
 CHAPTER 1
INTRODUCTION

The biggest organ of the body is human skin. Its weight lies between six and nine pounds and surface
area is about two square yards. Inner part of body is separated by skin from the outer environment. It
provides protection against fungal infection, bacteria, allergy, viruses and controls temperature of body.
Situations that frustrate, change texture of the skin, or damage the skin can produce symptoms like
swelling, burning, redness and itching. Allergies, irritants, genetic structure, and particular diseases
and immune system related problems can produce dermatitis, hives, and other skin problems. Many of
the skin diseases, such as acne, alopecia, ringworm, eczema also affect your look. Skin can also
produce many types of cancers. Image processing is used to detect these diseases by using various
methods like segmentation, filtering, feature extraction etc.
To get an improved image or to get meaningful information from an image, it is necessary to convert
an image into digital form and then perform functions onto that image. It is a part of signal processing.
The input is an image and it may be a video, a photograph and output is also another image having
same characteristics as input image. Mostly Image Processing models take input samples as 2-D signals
and after that they apply fixed signal processing methods to them. It is widely used technology now
days and it has various applications in the area of business. It is a new research area within engineering
and computer science too. The range of skin diseases is very wide.
Skin diseases have a serious impact on the psychological health of the patient. It can result in the loss
of confidence and can even turn the patient into depression. Skin diseases can thus be fatal. It is a
serious issue and cannot be neglected but should be controlled. So it is necessary to identify the skin
diseases at an early stage and prevent it from spreading. Human skin is unpredictable and almost a
difficult terrain due to its complexity of jaggedness, lesion structures, moles, tone, the presence of
dense hairs and other mitigating confusing features. Early detection of skin diseases can prove to be
cost effective and can be accessible in remote areas. Identifying the infected area of skin and detecting
the type of disease is useful for early awareness. In this paper, a detection system is proposed which
enables the users to detect and recognize skin disease.

1.1 BACKGROUND AND MOTIVATION

Now a day's people are suffering from skin diseases, more than 125 million people suffering from skin
diseases also skin disease rate is rapidly increasing over last few decades specially Melanoma is most
diversifying skin disease. Nevus rate is high specially at rural areas. If skin diseases are not treated at
earlier stage, then it may lead to complications in the body including spreading of the infection from

1
one individual to the other. The skin diseases can be prevented by investigating the infected region at
an early stage. The characteristic of the skin images are diversified, so that it is challenging job to
devise an efficient and robust algorithm for automatic detection of the skin disease and its severity.
Skin tone and skin color plays an important role in skin disease detection. Color and coarseness of skin
are visually different. Automatic processing of such images for skin analysis requires quantitative
discriminator to differentiate the diseases.
Proposed system is combo model which is used for the prevention and early detection of skin disease,
Melanoma and Nevus. Basically skin disease diagnosis depends on the different characteristics like
color, shape, texture etc. there are no accepted treatment for skin diseases Different physicians will
treat differently for same symptoms. Key factor in skin diseases treatment is early detection further
treatment reliable on the early detection.
In this paper, Proposed system is used for the diagnosis multiple skin disease using statistical parameter
analysis. Statistical analysis is anxious with analysis of random data. Random data is pattern of skin
diseases. Standard database is used this data does not have any mathematical expression; it has some
statistical properties. To analyses random data, we must analyse statistical properties of it.

1.2 CONTRIBUTION
In this paper, we present an image to diagnose multiple skin diseases using statistical parameter
analysis. Statistical analysis is concerned with the analysis of random data. This system is combomodel
which is to be used to diagnose multiple skin diseases at a time. The target skin diseases are Melanoma,
Nevus. The disease diagnosis and classification is built on statistical parameter analysis. Statistical
parameters includes: Entropy, Texture index, Standard deviation, Correlation fact
Depending on standard range of parameters skin disease is going to be diagnosed and classified.

1.3 PROBLEM STATEMENT

The doctors typically have assumed diagnosis opinion, which most likely begin by searching
for further evidence that their assumption can be validated and in cases where it is not validated, they
will have missed other potential diagnosis. Bias essentially influences analysis made by medical
practitioners, just as with any human search that begins with keywords chosen by the user.
Additionally, if a doctor begins searching by symptoms, while this may be accurate, the order or weight
given to any of the symptoms would most likely give a bias towards related diagnosis when in fact,
there may be a symptom that is not given any credit and thus not included in the search or considered
in timely fashion.
The heavy dependencies on medical expert for medical image diagnosis analysis are a serious
challenge for regions (especially Low- and Medium-Income Countries) where the expert might not be

2
readily available, inadequate or nonresponsive to an urgent medical need (such as
dermatologicalrelated). The aforementioned problems suggest that a better and manageable solution is
needed urgently with the view to minimize these dependencies and human bias, thus leading to our
research question.

1.4 DRAWBACK OF EXISTING SYSTEM

The algorithms used are SVM and CNN which fails to provide accurate results when the size of data
set is very high or if the dataset has greater amount of noise. The main drawback lays in their structural
simplicity, especially in case of complex skin diseases, like psoriasis or skin cancers, the pathogenesis
of which results from complicated interactions between cellular or molecular components.
1.5 PROPOSED SYSTEM
In this paper we propose the image analysis system to detect the skin disease. Our system captures
image from standard database and put into the system to inform the user for preventing the threats
linked to the skin diseases. More briefly we present the image analysis system to detect different skin
diseases where user will able to take image of different moles or skin patches. Our system will analyse
and process the image and classifies the image to normal Melanoma, Nevus and Basal Cell Carcinoma
case based extracting the image features. An alert will be provided to the user to seek medical help is
the mole belongs to a typical a Melanoma category.

Fig 1.1: Proposed System

3
1.6 ADVANTAGES:
Simple to implement
A simple random sample is used by researchers to statistically measure a subset of individuals
selected from a larger group or population to approximate a response from the entire group. This
research method has both benefits and drawbacks.

Less time consumption

In other words, companies do not structure and manage their projects to take advantage of gains in task
performance (tasks that are completed in less time than planned) in order to ensure they cancel out the
many delays in task performance (tasks that take longer than planned).
Less manpower required
The link between manpower and company projects is fairly simple: Manpower is proportional to
productivity. The more people are available to work, the faster projects can be completed or the more
projects a company can take on. Conversely, a lack of adequate manpower prevents businesses from
completing task.
Security of data
It acts as the first line of defense against security attacks and prevents them from causing damage to
your sensitive data. It takes care of a variety of security threats such as malware, viruses, spyware and
adware. Some even offer Email ID protection and prevent harmful downloads

4
 CHAPTER 2
EXISITNG SYSTEM
There are several existing systems for skin disease detection and diagnosis leveraging machine
learning (ML) and artificial intelligence (AI). These systems aim to provide accurate and efficient
diagnostic support to dermatologists and general practitioners, or sometimes even directly to patients.
Below are some examples and categories of such systems:

1. Image-Based Diagnosis Systems :

These systems analyze dermoscopic or clinical images of skin lesions to detect or classify skin
conditions such as melanoma, eczema, psoriasis, and acne.

Examples :
ISIC (International Skin Imaging Collaboration): Provides a benchmark dataset of dermoscopic images
for training ML models to classify skin lesions into benign and malignant categories.

DeepDerm: Uses convolutional neural networks (CNNs) to classify images into multiple skin diseases
with performance comparable to dermatologists.

Dermoscopy Analysis Tools: Applications like DermoScan or SkinVision allow users to upload images
for automated analysis of potential skin cancers.

2. Smartphone Applications :
These are user-friendly mobile apps that use ML to assess skin conditions based on photos taken with
smartphone cameras.
Examples :
SkinVision: Focuses on early melanoma detection by analyzing moles and lesions.

Miiskin: Helps users track changes in their skin over time using ML-assisted image comparisons.

Aysa: Provides AI-powered advice on skin symptoms based on photos and user input.

5
 CHAPTER 3
LITERATURE SURVEY

Image Analysis Model for Skin Disease Detection Alaa Haddad

Skin disease is the most common disease in the world. The diagnosis of the skin disease requires a
high level of expertise and accuracy for dermatologist, so computer aided skin disease diagnosis model
is proposed to provide more objective and reliable solution. Many researches were done to help detect
skin diseases like skin cancer and tumor skin. But the accurate recognition of the disease is extremely
challenging due to the following reasons: low contrast between lesions and skin, visual similarity
between Disease and non-Disease area, etc. This paper aims to detect skin disease from the skin image
and to analyze this image by applying filter to remove noise or unwanted things, convert the image to
grey to help in the processing and get the useful information. This help to give evidence for any type
of skin disease and illustrate emergency orientation. Analysis result of this study can support doctor to
help in initial diagnoses and to know the type of disease. That is compatible with skin and to avoid
side effects.
Classification of Skin diseases using Image processing and SVM N Vikranth Kumar;
P Vijeeth kumar; K Pramodh; Yepuganti Karuna IEEE 2019
Skin diseases such as Melanoma and Carcinoma are often quite hard to detect at an early stage and it
is even harder to classify them separately. Recently, it is well known that, the most dangerous form of
skin cancer among the other types of skin cancer is melanoma because it is much more likely to spread
to other parts of the body if not diagnosed and treated early. In order to classify these skin diseases,
"Support Vector Machine (SVM)" a Machine Learning Algorithm can be used. In this paper, we
propose a method to identify whether a given sample is affected with Melanoma or not. The steps
involved in this study are collecting labelled data of images that are pre-processed, flattening those
images and getting the pixel intensities of images into an array, appending all such arrays into a
database, training the SVM with labelled data using a suitable kernel, and using the trained data to
classify the samples successfully. The results show that the achieved accuracy of classification is about
90%.
Automatic Classification of Clinical Skin Disease Images with Additional High-Level
Position Information Jingyi Lin ;Zijian Guo ; Dong Li ; Xiaorui Hu ;Yun Zhang IEEE
2019
Since skin disease is one of the most common human diseases, intelligent systems for classification
of skin diseases have become a new line of research in deep learning, which is of great significance

6
for both doctors and patients. Some skin-disease datasets have already been published, such as the
SD198 dataset, which contains 6584 clinical skin-disease images of 198 categories.
However, because of the diversity of clinical dermatology, previous works have showed that the
performance of deep visual features is not as good as or even worse than hand-crafted features for skin
disease classification. In this paper, we propose an SD-198-P dataset, which includes additional
highlevel position information in the SD-198 dataset to guide the generation of better deep visual
features. Our experiment shows that, after adding the position information, the performance of deep
visual features is better than that of hand-crafted features. To the best of our knowledge, our method
outperforms the current state-of-the-art clinical skin disease classification methods.

Skin Disease detection based on different Segmentation Techniques Kyamelia Roy ;

Sheli
Sinha Chaudhuri ; Sanjana Ghosh ; Swarna Kamal Dutta ; Proggya Chakrabor IEEE
2019
The outer integument of the human body is skin. The skin pigmentation of human beings varies from
person to person and human skin type can be dry, oily, or combination. Such a variety in the human
skin provides a diversified habitat for bacteria and other microorganisms. Melanocytes in the human
skin, produces melanin which can absorb harmful ultraviolet radiation from sunlight which can damage
the skin and result in skin cancer. The necessary tools needed for early detection of these diseases are
still not a reality in most third world communities. If the symptoms of skin diseases such as acne,
dermatomyositis, candidiasis, cellulitis, Scleroderma, chicken pox, ringworm, eczema, psoriasis, etc.
are left untreated in its early stage then they can result in numerous health complications and even
death. Image segmentation is a technique which aids with the detection of these skin diseases. In this
paper, image processing techniques like adaptive thresholding, edge detection, K-means clustering and
morphology-based image segmentation have been used to identify the skin diseases from the given
image set. The acquired image set was pre-processed by deblurring, noise reduction and then
processed. Depending on the definite pattern (pertaining to a distinct disease) present in the processed
image the disease is detected at the output for a corresponding input image.
Soumya Sourav, Department of Electrical Engineering, Delhi Technological University
Abstract- Dermatological Diseases are one of the biggest medical issues in 21st century due to it's
highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In
cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the
probability of getting cured? We believe that the application of automated methods will help in early
diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present

7
a completely automated system of dermatological disease recognition through lesion images, a
machine intervention in contrast to conventional medical personnel-based detection.
Expert System for Diagnosis of Skin Diseases [81
Skin diseases are frequent diseases to every person and various types of infections are becoming very
frequent. You know that all of these diseases are very harmful, especially if not controlled at an early
stage. Skin diseases not only damage the skin. It can have a large effect on a person 's daily life, destroy
confidence of a person, hang their movement, and turn to depression. Sometimes, many people try to
treat these allergies by using their own therapy. However, if these methods are not appropriate for that
type of skin disease then it would make it more harmful. Skin diseases can easily transfer from human
to human so there is a need to control it their initial stage to prevent it from spreading. This paper
presents an implementation of a skin diseases diagnosis system which helps user to detect human skin
diseases and provides medical treatments timely. For this purpose, user will have to upload a disease
affected skin image to our system and give answers to the questions which are asked to user according
to the symptoms of the skin. These symptoms are used to identify the disease and provide a medical
treatment. This system works on technologies like image processing and data mining for skin diseases
detection. So the whole project is divided in to below major parts, Image preprocessing, segmentation
and feature extraction.
Classification model and skin disease predication.
Medical treatment suggestions or advice.

The image of skin disease is taken and various pre processing techniques are applied onto that
image for noise removal and image enhancement. This image is segmented by using a segmentation
technique i.e. thresholding segmentation. At last, data mining techniques are used to identify the skin
disease and to provide recommendations to users. This expert system pertain disease recognition
accuracy of 85% for Eczema, 95% for Impetigo and 85% for Melanoma. Both image based technique
and questionnaire technique help to increase reliability and performance of the system.

Limitations
This application is implemented only for three skin diseases (Eczema, Impetigo and
Melanoma).
It is implemented only for windows application so that is not yet develop for smart phones like
Android, IOS etc.
During image acquisition, the distance between camera lens and affected skin should be 5cm.
When capture the image for this application, it is mandatory to capture it without any light
effects.
8
 It only supports English language not for other ordinary languages like Sinhala,Tamil.
Online Children Skin Diseases Diagnosis System [91
Rule based and forward chaining inference engine methods are used to implement this model which
is used to identify the skin disease. By using this system, user is allowed to identify children skin
diseases via online and provide useful medical suggestions or advice timely. In this system, it consists
of diagnosis module, login module, info module, report module and management module.
There are two main modules called diagnosis and management module. In the diagnose
module questions are asked to the user and on the basis of answers given by the user, Children's
symptoms and condition are identified. This system may be an alternative for parents to identify skin
diseases of children, in response to the questions about the symptoms and the condition children'
skin.
Mobile-based Medical Assistance for Diagnosing Different Types of Skin Diseases Using
Case based Reasoning with Image Processing [111
In artificial intelligence (Al), medical field is a recent area for research purpose. This paper implements
a mobile based medical assistance which is used for diagnosing skin diseases by the use of CBR and
image processing. This model was developed to help users to pre- examine their skin situation whether
they have a disease or not. Also to increase the awareness of skin diseases on what it may do to our
bodies which will lead to death or infecting other people and have a cure before it gets worse. The
proposed system is successfully implemented to detect 6 different skin diseases with an accuracy of
90%. The scale of symptoms, which is used for testing, is 15%, for validation it is 10% and for testing
it is 75%. This supervised system identify diseases at the rate of 90% where the unsupervised system
detect diseases at the rate of
80%. The detection rate of the sample disease with the other related disease is as follows: Eczema —
88%; Psoriasis — 61%; Acne — 75%; Skin Cancer — 51%; Scabies — 43%; and Seborrhea Dermatitis
34%.
An Innovative Skin Detection Approach Using Color based Image Retrieval Technique
[121

The idea of "skin detection & quot; from an image is described as the categorization of the
existence pixels in that image into two skin and Non-skin classes. Many methods uses different color
space to extract features for the categorization of pixels, but most of these methods does not detect
different type of skin with high accuracy. The present method in this paper is implemented by using
quot; Color based image retrieval & quot; (CBIR) technique. In this method, first of all by finding
means of CBIR method and image tiling and finding the relationship between pixel and its neighbors,

9
a set of feature vector is prepared and then at the test stage, training is used for skin detection.
Experimental results show that the proposed model identifies different type of skin with a high
accuracy and it is not sensitive to illumination intensity and with the movement of face. The proposed
method contains two steps such as train and test. First in training step, pure skin images were trained
and then in testing steps skin area were detected from non- skin areas.

10
 CHAPTER 4
REQUIREMENT SPECIFICATION

4.1 System Requirement Analysis

The direct result of requirements analysis is Requirement's specification. Hardware requirements
specifications list the necessary hardware for the proper functioning of the project. Software
requirements specifications is a description of a software system to be developed, laying out functional
and non-functional requirements, and may include a set of use cases that describe interactions the users
will have the software. In software engineering, a functional requirement defines the function of a
system and its components. A function is described as a set of inputs, the behaviour, and outputs. A
non-functional requirement that specifies the criteria that can be used to judge the operation of a
system, rather than specific behaviour.

4.2 Functional Requirements

A function of software system is defined in functional requirement and the behaviour of the system is
evaluated when presented with specific inputs or conditions which may include calculations, data
manipulation and processing and other specific functionality.
The functional requirements of the project are one of the most important aspects in terms of entire
mechanism of modules. After validating our model, it should be able to predict the future stock market
price.

4.3 Non-Functional Requirements

Non-functional requirements describe how a system must behave and establish constraints of its
functionality. This type of requirements is also known as the system's quality attributes. Attributes such
as performance, security, usability, compatibility are not the feature of the system, they are a required
characteristic. They are "developing" properties that emerge from the whole arrangement and hence
we can't compose a particular line of code to execute them.
Any attributes required by the customer are described by the specification. We must include only those
requirements that are appropriate for our project. Some Non-Functional Requirements are as follows

- Reliability
The structure must be reliable and strong in giving the functionalities. The movements must be made
unmistakable by the structure when a customer has revealed a couple of enhancements. The
progressions made by the Programmer must be Project pioneer and in addition the Test designer.
- Maintainability
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The system watching and upkeep should be fundamental and focus in its approach. There should not
be an excess of occupations running on diverse machines such that it gets hard to screen whether the
employments are running without lapses.

Fig 4.3.1 Non-functional requirements

- Performance
The framework will be utilized by numerous representatives all the while. Since the system
will be encouraged on a single web server with a lone database server outside of anyone's ability to
see, execution transforms into a significant concern. The structure should not capitulate when various
customers would use everything the while. It should allow brisk accessibility to each and every piece
of its customers. For instance, if two test specialists are all the while attempting to report the vicinity
of a bug, then there ought not to be any irregularity at the same time.
-Portability
The framework should to be effectively versatile to another framework. This is obliged when
the web server, which s facilitating the framework gets adhered because of a few issues, which requires
the framework to be taken to another framework.

- Scalability
The framework should be sufficiently adaptable to include new functionalities at a later stage.
There should be a run of the mill channel, which can oblige the new functionalities.

12
4.4 Tools and Technology Details
Hardware Requirements
The most common set of requirements defined by any operating system or software application is the
physical computer resources, also known as hardware, a hardware requirements list is often
accompanied by a hardware compatibility list (HCL), especially in case of operating systems. An HCL
list tested, compatible, and sometimes in compatible hardware devices for a particular operating
systems or applications. The CPU is a fundamental system requirement for any software most software
running on different kinds of architecture defines processing power as the model and he clock speed
of the CPU. In this memory requirements are defined after considering demands of applications,
operating system, supporting software and files, and other running process. Hardware requirements
specifications list the necessary hardware for the proper functioning of the project.
System Processor: Pentium IV 2.4 GHz Hard Disk
Rom: 40 GB.
Ram: 512 MB.

Any desktop / Laptop system with above configuration or higher level.

Software Requirements
Software requirements deal with software resource requirements and prerequisites that need to
be installed on the computer to provide optimal functioning of an application. These requirements are
prerequisites are generally not included in the software installation package and need to be installed
separately before the software is installed. Software requirements specifications is a description of a
software system to be developed, laying out functional and non-functional requirements, and may
include a set of use cases that describe interactions the users will have the software. Operating System
Windows 7/ 8 / 10

Programming Language : Python

Framework : Anaconda, Jupyter Notebook
DLLibraries : Numpy, Pandas, OpenCV, Tensorflow, Keras.
System tool : VS Code

13
 CHAPTER 5
SYSTEM ANALYSIS
5.1 SYSTEM DESIGN
The proposed method includes the following 3 processes.
Preprocessing
Feature Extraction and Selection
Classification
The overall flow of the proposed method is represented in Figure. The performance of the
Naive Bayes is analyzed using the feature matrix. Further, the performance of the Hog is studied for
its accuracy, sensitivity and specificity values. The process of diagnosing the eye diseases is illustrated
in the upcoming sections.

Fig 5.1.1 data flow of proposed system

5.2 PRE-PROCESSING
Image pre-processing is the initial step to identify the affected area. Multiple steps are performed in
the preprocessing phase to make the image suitable for the feature extraction process. The
abnormalities in the input image are detected and preprocessed for the following purpose:
To avoid uneven illumination.
To enhance the contrast among image background pixels and exudates.
To eliminate the noise in the input image.

14
In this research work, the techniques used for the preprocessing phase are:
Image resizing.
Color transformation(RGB to Gray) and
Histogram equalization.

Image Resizing

An image size can be changed in several ways. One of the simpler ways of increasing image
size is nearest-neighbor interpolation, replacing every pixel with the nearest pixel in the output; for
up scaling this means multiple pixels of the same colour will be present. Image resizing is necessary
when you need to increase or decrease the total number of pixels, whereas remapping can occur when
we are correcting for lens distortion or rotating an image. Zooming refers to increase the quantity
ofpixels, so that when you zoom an image, we will see more detail.
Color Transformation
The retinal images are taken from the fundus camera in the form of RGB (Red, Green, and
Blue). Grayscale is a range of shades of gray without apparent color. The darkest possible shade is
black, which is the total absence of transmitted or reflected light. The lightest possible shade is white,
the total transmission or reflection of light at all visible wavelengths. Intermediate shades of gray are
represented by equal brightness levels of the three primary colors (red, green and blue) for transmitted
light for reflected light. In the case of transmitted light (for example, the image on a computer display),
the brightness levels of the red (R), green (G) and blue (B) components are each represented as a

(RGB) grayscale image, R = G = B. The lightness of the gray is directly proportional to the number
representing the brightness levels of the primary colors.
Histogram Equalization
The use of fundus camera to capture the skin disease image results in an uneven illumination. The
portions near the center are well illuminated and hence it looks very bright. But the porti ons on the
sides are less illuminated and hence looks very dark. To address this issue, the histogram equalization
is used. As the regions of exudate and optic disc are much greater in intensity than the neighboring
regions of the image, the histogram equalization method is used to assign the neighboring regions
greater intensity.
Adaptive Histogram Equalization
Adaptive Histogram Equalization differs from ordinary histogram equalization in the respect that the
adaptive method computes several histograms, each corresponding to a distinct section of the image,

15
and uses them to redistribute the lightness values of the image. It is therefore suitable for improving
the local contrast and enhancing the definitions of edges in each region of an image.
Contrastive Limited Adaptive Equalization
Contrast Limited AHE (CLAHE) differs from adaptive histogram equalization in its contrast limiting.
In the case of CLAHE, the contrast limiting procedure is applied to each neighborhood from which a
transformation function is derived. CLAHE was developed to prevent the over amplification of noise
that adaptive histogram equalization can give rise to.

5.3 ALGORITHMS USED IN FEATURE EXTRACTION

The HOG features are extracted from the localized ROI. The HOG features are invariant to geometric
and photometric transformation and thus used to describe the shape and edge of the structures present
within the image. As HOG features are related to edge information, the skin deformation due to the
presence of skin diseases can be depicted with these features. Deformation in the skin is one of the key
parameters in the detection of skin disease. To compute the HOG features, the image is divided into
small cells and the shape of the objects is obtained by counting the strength and orientation of the
spatial gradients in each cell.
Histogram of Gradient (HOG)
The HOG extracts the features of the images that are present over the grid of overlapping rectangular
blocks in the search window. The histogram of each block is used to describe the frequency of the
gradient directions inside each block. The image is generally described by a set of local histograms.
These histograms count the occurrences of the gradient orientation and they become the local parts of
the images. The steps involved in calculating the histogram are:
• Computing the gradients of the image
• Constructing the histogram orientation of each cell

• Normalizing the histograms in each block of the cells

A histogram of oriented gradients (HOG) is used in image processing applications for detecting objects
in a video or image, which by definition is a feature descriptor [2], proposed by Dalal and Triggs who
used their method for pedestrian detection. Figure shows the block diagram and block normalization
scheme of HOG feature extraction.

16
 Input Image

Classifica on

FIG 5.3.1 HOG algorithm

Gradient Computa on
By applying two one-dimensional filtering techniques, the gradient of the image is easily obtained.
The calculated gradient can be either signed or unsigned. The next step involves the orienta on
binning. Based on the number of bins, a histogram is calculated for each cell. This method is used to
0. split the image into various cells. Each cell has a spa al region with a predetermined size of pixels.
During each orienta on, the HOG is calculated by gathering the number of feature values (votes) into
bins. The histogram considers the gradient at each point. By considering the magnitude of the
gradient, the edges are weighted. The gradient orienta ons around the edges are more prominent
than the uniform regions. The increase in the number of bins, in turn, increases the details of the
histogram.
Based on the gradient distribu on within the pixel patches, mul ple feature descriptors are available.
The HOG are the feature descriptors to detect the object and it counts occurrences of gradient
orienta on in the localized image por ons. This process is same as the edge orienta on histograms,
contrasts of shape and the descriptors of scale-invariant feature transform. As preprocessing provides
a slight impact on the performance, the HOG ensures normalized color and gamma values by
compu ng the gradient values.

17
SVM
An SVM is a classifica on-based method or algorithm. There are some cases where we can use it for
regression. However, there are rare cases of use in unsupervised learning as well. SVM in clustering is
under research for the unsupervised learning aspect. Here, we use unlabelled data for SVM.
Since the topic is under research, we will only look at what it means. In regression, we call the concept
SVR or support vector regression. It is quite similar to SVM with only a few changes. However, it
is more complicated than SVM.
Now, we come to SVM. It is a strong data classifier. The support vector machine uses two or more
labelled classes of data. It separates two different classes of data by a hyperplane. The data points based
on their position according to the hyperplane will be put in separate classes. In addition, an important
thing to note is that SVM in Machine Learning always uses graphs to plot the data. Therefore, we will
be seeing some graphs in the article. Now, let's learn some more stuff.
Parts of SVM in Machine Learning
To understand SVM mathematically, we have to keep in mind a few important terms.
These terms will always come whenever you use the SVM algorithm. So let's start looking at them one
by one.
Support Vectors
Support vectors are special data points in the dataset. They are responsible for the construction of the
hyperplane and are the closest points to the hyperplane. If these points were removed, the position of
the hyperplane would be altered. The hyperplane has decision boundaries around it. And, the support
vectors help in decreasing and increasing the size of the boundaries. They are the main components in
making an SVM. We can see the picture for this.

Support Vector Machines

Maximum Size
of Margin

FIG 5.3.2 SUPPORT VECTOR MACHINE

The yellow and green points here are the support vectors. Red and blue dots are separate
classes. The middle dark line is the hyperplane in 2-D and the two lines alongside the hyperplane are
the decision boundaries. They collectively form the decision surface.

18
Decision Boundaries
Decision boundaries in SVM are the two lines that we see alongside the hyperplane. The
distance between the two light-toned lines is called the margin. An optimal or best hyperplane form
when the margin size is maximum. The SVM algorithm adjusts the hyperplane and its margins
according to the support vectors.
Hyperplane
The hyperplane is the central line in the diagram above. In this case, the hyperplane is a line because
the dimension is 2-D. If we had a 3-D plane, the hyperplane would have been a 2-D plane itself.
There is a lot of mathematics involved in studying the hyperplane. We will be looking at that. But, to
understand a hyperplane we need to imagine it first. Imagine there is a feature space (a blank piece of
paper). Now, imagine a line cutting through it from the center. That is the hyperplane. The math
equation for the hyperplane is a linear equation.

ao + alxl + a2x2 +……...+ anxn

This is the equation. Here ao is the intercept of the hyperplane. Also, al and a2 define the first and
second axes respectively. Xl and X2 are for two dimensions. Let us assume that the equation is equal
to E. So if the data points lie beneath the hyperplane then E<O. If they are above it, the E>=O. This is
how we classify data using a hyperplane.
In any ML method, we would have the training and testing data. So here we have n*p matrix which
has n observations and p dimensions. We have a variable Y, which decides in which class the points
would lie. So, we have two values 1 and -1. Y can only be these two values in any case. If Y is 1 then
data is in class 1. If Y is -1 then data is in class -1.
Naive Bayes Classifier
The Naive Bayes classifier is an efficient and simple probabilistic classifier based on
Bayes' theorem. It is based on the Bayes Theorem. It predicts the class membership probabilities.
"Naive Bayesian classifiers assume that the effect of an attribute value on a given class is independent
of the values of the other attributes. This assumption is called class conditional independence". Due to
this assumption, the computation of the NB classifiers is better than the other classifiers. It is a simple
model that assigns class labels from a finite set to a vector of feature values. These classifiers assume
that the value of a particular feature is independent of any other feature. The advantage of naive Bayes
is that it requires only a small number of training data. With the small number of training data, the
parameters can be estimated for classification. It classifies the data in two phases namely training phase
and prediction phase of that sample belonging to each class.
The Naive Bayes method is suitable for the discrete valued attributes as well as for large size
dataset, but in case of continuous valued attributes, Naive Bayes method is lacking in attribute
19
interactions. On the other hand, the decision tree does not give good performance when the data size
is very large. These limitations have been overcome by the notion of NB Tree algorithm. Proposed a
hybrid algorithm called Naive Bayes Tree, which is a hybrid approach appropriate in learning
environment when various attributes are likely to be relevant for a classification task. NB Tree gives
relaxation to the attribute independence assumption of the Naive Bayes algorithm. "NB Tree is a
hybrid classification technique which combines Decision Tree and Naive Bayes classification
algorithms. The algorithm is similar to the classical recursive partitioning schemes except that the leaf
nodes created are Naive-Bayes categorizers instead of nodes predicting a single class and the learned
knowledge is represented in the form of a tree. It combines the advantage of both Decision Tree and
Naive Bayes Classification." NB Tree induces highly accurate classifiers in practice. It has been shown
that NB Tree is accurate and scale up in terms of accuracy on real world datasets.
Naive Bayes algorithm
It is a classification technique based on Bayes' Theorem with an assumption of independence among
predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature
in a class is unrelated to the presence of any other feature.
For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in
diameter. Even if these features depend on each other or upon the existence of the other features, all
of these properties independently contribute to the probability that this fruit is an apple and that is
why it is known as 'Naive'.
Naive Bayes model is easy to build and particularly useful for very large data sets. Along with
simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.
Bayes theorem provides a way of calculating posterior probability P(clx) from P(c), P(x) and P(xlc).
P(clx) is the posterior probability of class (c, target) given predictor (x, attributes).

P(c) is the prior probability of class.

P(xlc) is the likelihood which is the probability of predictor given class.

P(x) is the prior probability of predictor.

20
 CHAPTER 6
SYSTEM DESIGN
System design is the process of planning a new system to compliment or all together replace
the old system. The purpose of the design phase is the first step in moving from the problem domain
to the solution domain. The design of the system is the critical aspect that affects the quality of the
aspects of the system into physical aspects of the system. It is the process of defining the architecture,
modules, interfaces, and data for a system to satisfy specified requirements. System design could be
seen as the application of system theory product development. There is some overlap with the
disciplines of system analysis, system architecture, and system engineering.

6.1 DATA FLOW DIAGRAM

A data flow diagram (DFD) is a graphical representation of the flow of data through an
information system, modelling its process aspects. A DFD is often used as a preliminary step to create
an overview of the system without going into great detail, which can later be elaborated. DFDs can
also be used for the visualization of data processing. DFD shows what kind of information will be
input to and output from the system, how the data will be advanced to the system, where the data will
be stored. It does not show information about process timing on weather processes will operate in
sequence or in parallel, unlike a traditional structured flowchart which focuses on control flow, or a
UML activity workflow diagram, which presents both control and data flows as a unified model.
The dataflow diagram is also known as bubble charts. DFD is a designing tool used in the top-down
approach to systems design. The DFDs can be used to provide the end user with the physical idea of
where the data the input ultimately has an effect upon the structure of the whole system from order to
dispatch to report.
ENROLNIENT VERIFICATION

FIG 6.1.1 DATA FLOW

21
In Fig 6.1 there are mainly two stages i.e., Enrollment and Verification respectively. In the enrollment
and verification stage the image can be preprocessed and improves the low contrast image, it also
includes the image enhancement, resizing of the image. After preprocessing the feature of the image
can be extracted, Hog features are extracted from the localized ROI. The feature extraction followed
by feature selection, In this the ROI is located using a rectangular mask and this mask is selected by
feature matrix, then finally the selected image is classified using Naive Bayes classifier and SVM
classifier to detect the disease.

6.2 ACTIVITY DIAGRAM

Activity diagram is defined as a UML diagram that focuses on the execution and flow of the behavior
of a system instead of implementation. Activity diagrams consist of activities that are made up of
actions which apply to behavioral modeling technology. It is a behavior that is divided into one or more
actions. Activities are a network of nodes connected by edges. There can be action nodes, control
nodes, or object nodes. Action nodes represent some action. Control nodes represent the control flow
of an activity. Object nodes are used to describe objects used inside an activity. Edges are used to show
a path or a flow of execution. Activities start at an initial node and terminate at a final node.

6.2.1 Activity Diagram

22
6.3 USE CASE DIAGRAM
It represents the functionality of a system by utilizing actors and use cases. It encapsulates the
functional requirement of a system and its association with actors. It portrays the use case view of a
system.

Des

on

Fig. 6.3. Use Case Diagram

6.4 SEQUENCE DIAGRAM

It shows the interactions between the objects in terms of messages exchanged over time. It delineates
in what order and how the object functions are in a system.

23
 Fig. 6.4. Sequence Diagram
6.5. Communication Diagram
It shows the interchange of sequence messages between the objects. It focuses on objects and their
relations. It describes the static and dynamic behavior of a system.

1. Through Flask 2. Iden fy

Classify
Image

Make
Predic on

5. Make
Recommenda on

FIG 6.5. Communication Diagram

24
 CHAPTER 7
IMPLEMENTATION & RESULTS
7.1 METHODOLOGY
The image is initially pre-processed and Resize, Histotrophic Equalization (HE) in image acquisition.
The HOG (Histogram of gradients) features are extracted from Collective competitive ratio and
number of statistical properties is derived. The derived properties constitute the HOG features that are
fed to the Naive Bayes classifier and SVM classifier for identifying the diseases. The classifier is
trained and tested with disease image dataset. The methodology of the proposed methodology is shown
in Fig.7. I
Image Acquisition Noise Removal Feature Extraction using HOG Classification

CLASSIFICATION USING
SVM AND NAVIE BAYES

Fig 7.1: Methadology

Image Acquisition
The first stage of our automated image analysis system is image acquisition. This stage is essential for
the rest of the system; hence, if the image is not acquired satisfactorily, then the remaining components
of the system may not achievable, or the results will not be reasonable. In this stage first image system
requires the resized image for the better results. Input image given to the system is in RGB form. But
for our proposed system requires gray images. Hence using RGB to GRAY conversion in MATLAB
we convert RGB images in to Gray images.

25
Noise Removal
It's necessary to have quality images without any noise to get accurate result. Noisy images may lead
your algorithm towards incorrect result. Hence it becomes necessary to denoise the image. Image de
noising is an important image processing task; there are many ways to de noise an image. The important
for good image de noising model is that it will remove noise while preserving edges. Traditionally,
linear model have been used. To de-noise the image we can use median filter. Median filter does the
work of smoothening of images.
Feature Extraction
To get an accurate result in biomedical image processing it is always necessary that biomedical image
must be a very good quality. However, practically this is not easy. Due to different reasons obtain low
or medium quality images. Hence it becomes necessary to improve their quality. To improve the
quality of an image using image enhancement algorithm. This algorithm enhances the image by
focusing on parameters like contrast, brightness adjustment.
Classification
The overall flow of the proposed method is represented in Figure. The performance of the Naive Bayes
is analyzed using the feature matrix. Further, the performance of the Hog is studied for its accuracy,
sensitivity and specificity values. The process of diagnosing the skin diseases is illustrated in the
upcoming sections.

Fig.7.1.1: Architecture of Proposed System

26
Data Set Image

27
 Fig. 7.1.2. Dataset

Naive Bayes Classifier

Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities
for each class such as the probability that given record or data point belongs to a particular class. The
class with the highest probability is considered as the most likely class. This is also known as
Maximum A Posteriori (MAP). The MAP for a hypothesis is:
MAP(H) =
max( P(HIE) )
= max(

P(E) is evidence probability, and it is used to normalize the result. It remains same so, removing it
won't affect.
Naive Bayes classifier assumes that all the features are unrelated to each other. Presence or absence
of a feature does not influence the presence or absence of any other feature. We can use Wikipedia
example for explaining the logic i.e., A fruit may be considered to be an apple if it is red, round, and
about 4" in diameter. Even if these features depend on each other or upon the existence of the other
features, a naive Bayes classifier considers all of these properties to independently contribute to the
probability that this fruit is an apple.
P(HIMu1tip1e Evidences) = P(EII P(E21H) * P(H) / P(Multip1e Evidences)
In this research, diabetic retinopathy method is used to diagnose the Diabetic Retinopathy (DR).
Initially, the dataset images are resized and histogram equalization is applied. Then the key features
from the preprocessed images are extracted using the Histogram of Gradient (HoG). Then from HOG
features model is constructed using navis bayes algorithm.
The dataset is used here is binrushed which consists of 4 classes of diseases. Total numbers of images
are 1285. When we tested with testing test for 4 classes it shows 90.02% of accuracy. To get more
number of disease classes we divided the images into 8 classes. Navis Bayes algorithms showed overall
accuracy of 77.23% even though other algorithms for multiclass classification failed to cross 50%. We
also tested various scenarios for login pages, different types of images. Our algorithm proven better
results for most of the cases.

28
7.2 SAMPLE CODE INDEX.HTML
APP.HTML
from flask import render template, jsonify, Flask, redirect, url for, request, make_response
import os
import io import numpy as np
from PIL import Image
import keras.utils as image
from keras.models import model from_json app =
SKIN CLASSES = {
0: 'Actinic Keratoses (Solar Keratoses) or intraepithelial Carcinoma (Bowen's disease)',
l : 'Basal Cell Carcinoma',
2: 'Benign Keratosis',
3: 'Dermatofibroma',
4: 'Melanoma',
5: 'Melanocytic Nevi',
6: 'Vascular skin lesion'

@app.route('/')
def index() :
return render_template('index.html')
@app.route('/signin')
def signin():
return render template('signin.html')
@app.route('/signup')
def signup():
return render template('signup.html')
@app.route('/dashboard', methods=['GET', 'POST'])
def dashboard() :
return render template('dashboard.html') def
findMedicine(pred) :
if pred == 0:

29
 return"fluorouracil"
elif pred return
"Aldara" elif pred -2:
return "Prescription Hydrogen Peroxide"
elif pred
return "fluorouracil"
elif pred
return "fluorouracil (5-FU):"
elif pred
return "fluorouracil"
elif pred return "fluorouracil"
@app.route('/detect', methods=['GET', 'POST'])
def detect() :
json_response = { }
if request.method 'POST':
try:
file = request.files['file']
except KeyError:
return make_response(j sonify( {
'error': 'No file part in the request',
'code': 'FILE',
'message': 'file is not valid'
400)
imagePil = Image.open(io.BytesIO(file.read()))
# Save the image to a BytesIO object
imageBytesIO = io.BytesIO()
imagePil.save(imageBytesIO,format='JPEG')
imageBytesIO.seek(0)
print("detected ")
path imageBytesIO
fi_le = open('model.json', 'r')
loaded_json_model = j file.read()
j_file.close()
model = model son_model)

30
 model.load_weights('model.h5')
img image.load_img(path,
target_size=(224, 224))
img = np.array(img) img=img.reshape((l,224,224,3))
img = img/255
prediction=model.predict(img)
pred=np.argmax(prediction)
disease=SKIN_CLASSES[pred]
accuracy=prediction[0][pred]
accuracy=round(accuracy*100,2)
medicine=findMedicine(pred)
json response = {
"detected": False if pred == 2 else True,
"disease": disease,
"accuracy". accuracy,
"medicine" : medicine,
"img_path": file.filename,

return make_response(jsonify(json_response), 200)

else:
return render_template('detect.html')
if name mam
app.run(debug=True, port=3000)
INDEX.HTML
html> <html
lang="en">
<head>
<meta charset="UTF-8"> <link rel="icon" type="image/x-icon" href=" { { url for('static',
filename='images/favicon.ico')

<title>MedicineAI</title>
<meta name="description" content="Skin Disease detection and treatment management
susceptibility by Al">

31
 <meta name="viewport" content="width=device-width, initial-scale=l .0"
SEO Meta Tags -->
<meta name="keywords" content="medicine, Al, disease detection, treatment management">
<meta name="author" content="MedAI">
<meta name="robots" content="index, follow">
<!-- OpenGraph Meta Tags -->
<meta property="og:title" content="MedicineAI">
<meta property="og:description" content="Skin Disease detection and treatment management
susceptibility by Al">
<meta property="og:image" content="/static/images/og-image.jpg">
<meta property="og:url" content="https://medai.onrender.com">
<meta property="og:type" content="website">
<!-- CSS Stylesheets -->
<link rel="stylesheet" href="/static/css/index.css">
<link rel="stylesheet" href="/static/css/styles.css">
<!-- JavaScript -->
<script </head>
<body>
<div class="upper">
<!-- <img src="/static/images/medAi 2.png"> -->
<img src="/static/images/logo.png">
<p>Skin Disease detection and treatment management susceptibility by Al</p>

<button Get started</button>

</div>
</body>
</html>
SIGNIN.HTML
html> <html
lang="en">
<head>

32
 <meta
<link rel="icon" type="image/x-icon" href=" { { url for('static', filename='images/favicon.ico') } }
<meta name="viewport" content="width=device-width, initial-scale=l .0">
<title>sign in</title>
<link rel="stylesheet" href="/static/css/sign.css">
<link rel="stylesheet" href="/static/css/styles.css">
</head>
<body>
<a href="/">
<img src—"/static/images/logo.png" class—

<div class="center">
<hl>Sign in</hl>
<form action="dashboard" method="post">
<label for="E-mail">E-mail</label><br>
<input id="E-mail" type="text" placeholder="Please enter your email"> <br>
<label for="password">password</label><br>
<input id—"password" type—"password" placeholder="Please enter your password"><br>
<div class="btn div"
<button id="btn" onclick="location.href='welcome.html';">login</button>
<div class="singup_link">
<h3> Not a member?
</h3>
<a href="/signup">sing up here</a>
</div>
</div>
</div>
</form>
</body>

</html>

33
SIGNUP.HTML
html>
<html lang="en">
<head>
<meta charset="UTF-8">
<link rel="icon" type="image/x-icon" href=" { { url for('static', filename='images/favicon.ico') } }
<meta name="viewport" content="width=device-width, initial-scale=l .0">
<title>sign in</title>
<link rel="stylesheet" href="/static/css/sign.css">
<link rel="stylesheet" href="/static/css/styles.css">
</head>
<body>
<a href="/">
<img src="/static/images/logo.png" class—

<div class="center">
1 >Sign in</hl>
<form action="dashboard" method="post">
<label for="E-mail">E-mail</label><br>
<input id="E-mail" type="text" placeholder="Please enter your email"> <br>
<label for="password">password</label><br>
<input id="password" type="password" placeholder="Please enter your password"><br>
<div class="btn div">
<button id="btn" onclick="location.href='welcome.html';">login</button>
<div class="singup_link">
<h3> Not a member?
</h3>
<a href="/signup">sing up here</a>
</div>
</div>
</div>
</form>

34
</html>
INFO.HTML
html>
<html lang="en">
<head>
<meta charset="UTF-8">
<link rel="icon" type="image/x-icon" href=" { { url_for('static', filename='images/favicon.ico')

<meta name="viewport" content="width=device-width, initial-scale=l .0">

<title>info</title>
<link rel="stylesheet" href="/static/css/info.css">
<link rel="stylesheet" href="/static/css/styles.css">
</head>
<body>
<div class—"center">

<form action="contactform." method="post">

<div class="info">
<label for="ID">Enter your ID:</label><br>
<input id="ID" type="number" placeholder="Please enter your ID"> <br>
</div>
<div> <img src="unsplash aEaohrsWexg.png"></div>
<button id="btn2" onclick="location.href='drobPhoto.html';"> Start Scanning</button>
</form>
</div>
</body>
</html>

35
7.3 ACCEPTANCE TESTING
User Acceptance Testing is a critical phase of any project and requires significant participation by the
end user. It also ensures the system meets the functional requirements.
1
Test Case No.

Name of Test Upload Image File

User should selects the original Skin Disease

Test Case Description image

Sample Input
Skin Disease image

Output Image will be uploaded

7.4 SCREENSHOTS

Fig 7.4.1 interface

36
 Fig 4.7.2 sign-in page

FIG 6.4.3 opening page

37
Fig 4.7.4 skin disease type 1

Fig 4.6.5 skin disease type 2

38
Fig 4.7.6 skin disease type 3

39
 CHAPTER-8
CONCLUSION AND FUTURE WORK

Detection of skin diseases is avery important step to reduce death rates, disease transmitrions and
development of the skin disease. Clinical procesdures to detect skin diseases are very expensive and
time consuming. Image processing techniques helps to build automated screening system for
dermatology at an initial stage. The extraction of features plays a key role in helping to clacify skin
diseases.
In this reaseach the method of detection was designed by using pre trained SVM abnd navie bayas. In
conclusion, we must not forget that this research has an effective role in the detection skin diseases in
soudhi Arabia because it has very hot weather for the presence oh=f weather: thses indicate =s that
skin diseases are widw spread. The reaserch supports medical efficiency in soudhi Arabia.
Future enhancement
Futuree scopes of improment in present methodologies are
• A common model should be adopted for the identification of all types of skin disesases
• Support for muiltilingualism to develop user-freidlyness.
• To expand the muilti platform capability throuth an introduction to ios compatability.

40
 REFERENCES

[1] Mr. Pati1.S.P, Mr.Kumbhar.V.P, Mr.Yadav.D.R, Ms.Ukirade.N.S Detection by

Image Processing International Journal of Advanced Research in Electronics and Communication
Engineering (IJARECE)Volume4, Issue 4, Apri12015.
[2] KenPernezny, MonicaElliott, Aaron Palmateer and Nikolavranek Guidelines for Identification
and Management of Plant Disease Problems: Partll. Diagnosing Plant Diseases Caused by Fungi,
Bacteria and Viruses UFIFAS Extension.
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Conceptions on Computing and InformationTechnologyVol.3,

Issue.3,Oct0ber'2015; ISSN:2345-9808.

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