Industrial Internship Report

on
Deep Learning
at NIT Kurukshetra

submitted in the partial fulfillment of the requirements for the

award of the degree of
Bachelor of Technology

in
Computer Science & Engineering
by
Amit Shukla
Roll No. 202065
Batch 2020-24

Department of Computer Science & Engineering Central

University of Haryana
Jant Pali, Mahendergarh 123031, Haryana
Industrial Internship 2024
Acknowledgments

I want to express my profound gratitude to Dr. Rakesh Kumar, HoD

of the Department of Computer Science & Engineering, and Prof.
Phool Singh, Dean of the School of Engineering and Technology,
Central University of Haryana for their contributions to the
completion of my Industrial Internship titled ”Data Science and
Machine Learning Intern at “NIT KURUSHETRA”. I want to
express my special thanks to our mentor Dr. Shweta Sharma for the
time and efforts she provided throughout the internship, whose advice
and suggestions were really helpful to me during the Internship. In
this aspect, I am eternally grateful to her. The success of this project
is attributed to the contributions of dataset providers, open-source
libraries, and collaborators who have supported and enriched the
development of this research initiative. Their efforts have been
instrumental in advancing the field of automated plant disease
detection.
I am extremely grateful to my department staff members
and friends who helped me complete this internship.

Name
Amit Shukla
202065
About Institute

National Institute of Technology Kurukshetra (NIT Kurukshetra), an

institution of national importance, is one of the premier engineering
institutes in India. Established in 1963 as a Regional Engineering
College (REC), it was later upgraded to a National Institute
of Technology in 2002, reflecting its significant contribution to the
field of technical education and research. The institute is deeply
committed to research and development, encouraging faculty and
students to engage in innovative projects. It houses several research
centers and labs equipped with state-of-the-art facilities to support
cutting-edge research in various fields of science and technology. NIT
Kurukshetra envisions being a global center of excellence in technical
education and research, producing ethically sound and socially
responsible professionals. Its mission includes promoting innovation,
fostering an inclusive learning environment, and contributing to the
nation's technological and economic development. With its rich
legacy, commitment to excellence, and focus on research and
innovation, NIT Kurukshetra continues to be a beacon of knowledge
and a preferred destination for aspiring engineers and researchers. The
institute's dedication to shaping the future of technical education in
India and beyond makes it a pivotal player in the global academic
landscape.

Contact Details

National Institute of Technology

Kurukshetra.
Thanesar, Kurukshetra,
Haryana - 136119, India
Official Email: registrar@nitkkr.ac.in
 Components of the Project-

Pneumonia Detection from Chest X-Ray Images Using

Transfer Learning

1.1. Domain: Computer Vision, Machine Learning

1.2. Sub-Domain: Deep Learning, Image Recognition
1.3. Techniques: Deep Convolutional Neural Network,
ImageNet, Inception
1.4. Application: Image Recognition, Image Classification,
Medical Imaging

1. Description
2. Detected Pneumonia from Chest X-ray images using a
Custom Deep Convolutional Neural Network and by
retraining the pre-trained model “InceptionV3” with 5856
images of X-ray (1.15GB).

3. For retraining removed output layers, frozen first few

layers, and fine-tuned model for two new label classes
(Pneumonia and Normal).

4. Custom Deep Convolutional Neural Network attained a

testing accuracy of 89.53% and a loss of 0.41.

Dataset Name: Chest X-Ray Images (Pneumonia)

Number of Class: 2
Number/Size of Images:
1. Total: 5856 (1.15 Gigabyte (GB))
2. Training: 5216 (1.07 Gigabyte (GB))
3. Validation: 320 (42.8 Megabyte(MB))
4. Testing: 320 (35.4 Megabyte (MB))Model Parameters:
Machine Learning Library: Keras
Base Model : InceptionV3 && Custom Deep Convolutional Neural
Network
Optimizers: Adam
Loss Function: categorical_crossentropy

For Custom Deep Convolutional Neural Network:

Training Parameters
Batch Size: 64
Number of Epochs: 30
Training Time: 2 Hours

Output (Prediction/ Recognition / Classification Metrics)

Testing:

Accuracy (F-1) Score: 89.53%

Loss: 0.41
Precision: 88.37%
Recall (Pneumonia): 95.48% (For positive class)

Tools / Libraries:

Languages: Python
Tools/IDE : Anaconda
Libraries: Keras, TensorFlow, Inception, ImageNe
Onboarding E-Mail
 ACKNOWLEDGEMENT

I would like to thank Prof. Phool Singh, the dean of the Central University of
Haryana's School of Engineering and Technology, for his steadfast support and
encouragement during this endeavor. His vision and guidance have guided my
academic trajectory.

I would like to express my sincere gratitude to Dr. Rakesh Kumar, the head of
the Department of Computer Science and Engineering, for his tremendous
advice and assistance. I have been greatly motivated by his profound knowledge
of the topic and his capacity to motivate pupils.

I would especially like to express my gratitude to our mentor, Dr Shweta

Sharma Mam, for all of her hard work and dedication during the internship. Her
wise advice and suggestions have been really helpful, and we are really grateful
for her willingness to help and her tolerance. Her knowledge and direction have
been invaluable to the accomplishment of this project, and I will always be
grateful for her help.

Without the cooperative efforts of my colleagues, whose advice and support

have been crucial, this project would not have been feasible. I want to express
my gratitude to them for their cooperation, encouragement, and support.

Additionally, I would like to thank the dataset suppliers and several open-source
libraries for their contributions. Their resources have greatly aided and enriched
this project's growth. We especially appreciate the commitment to open research
and technology shown by the developers and maintainers of these programs and
datasets.

Finally, I want to express my gratitude to my friends and family for their

unwavering encouragement and support. Throughout this journey, their
confidence in my ability has served as a source of inspiration and strength.
 ABSTRACT

The XRay-Pneumonia-Detection project represents a convergence of cutting-

edge deep learning techniques, user-friendly graphical interface development,
and crucial advancements in medical image analysis. This initiative aims to
streamline and enhance the process of pneumonia detection in X-ray images
through the utilization of Convolutional Neural Networks (CNNs). By offering
a sophisticated yet accessible platform, this project empowers medical
practitioners to swiftly and accurately diagnose pneumonia cases, ultimately
contributing to improved healthcare outcomes.

Combining datasets sourced from reputable repositories, including Kaggle and

Mendeley Data, the project encompasses a curated compilation of chest X-ray
images, encompassing both bacterial and viral pneumonia cases, for robust and
comprehensive training.

The heart of this project lies in the meticulously designed CNN architecture.
Comprising multiple convolutional layers, max-pooling layers, and dense
layers, this model excels in classifying X-ray images into 'Normal' and
'Pneumonia' categories. Detailed analysis, comprehensive documentation, and
evaluation metrics showcased in the accompanying notebook provide an
insightful understanding of the model's performance, including accuracy, loss,
and visualization of results.

Users can effortlessly upload X-ray images, allowing rapid analysis and prompt
results. The intuitive drag-and-drop functionality simplifies the diagnostic
process, offering a glimpse into the future of medical technology's intersection
with user-centric design.

vi
 List of Figures

Fig 5.1 Model Training 13

Fig 5.2 Building a Confusion Matrix 14
Fig 5.3 Confusion Matrix 14
Fig 5.4 Performance Analysis 15
Fig 5.5 Datasets 16
Fig 5.6 Data Preprocessing 17
Fig 5.7 Visualize Dataset 17
Fig 5.8 Flowchart 18
Fig 6.1 Command for launching the application 20
Fig 6.2 Loading Page 21
Fig 6.3 Drag & Drop Page 21
Fig 6.4 Prediction Pneumonia Detected 22
Fig 6.5 Prediction Normal Detected 22
Fig 6.6 Model Information 23
Fig 6.7 Exported Image 23

vii
 Table of Contents

Certificate i
Internship Certificate ii
Declaration iii
Acknowledgment iv
Abstract V
List of Figures vi
Chapter 1 Overview of the Company 1
1.1 Purpose of internship 1
1.2 Research and Development 1
1.3 About us 2
Chapter 2 Overview of the Department 3
2.1 It includes the details about the work being carried out in each department 3
2.2 List the technical specifications of major equipment used in each department 3
2.3 Explain in detail about each stage of production 4
Chapter 3 Introduction to Project 6
3.1 Summary of Project 6
3.2 Purpose 6
3.3 Objective 6
3.4 Scope 7
3.5 Technology and Literature Review 8
3.6 Project / Internship Planning 8
Chapter 4 System Analysis 10
4.1 Study of Current System 10
4.2 Algorithms & Techniques 10
4.3 Dependencies 11
4.4 Features of the Proposed System 11
Chapter 5 System Design 13
5.1 System Design & Methodology 13
5.2 Datasets 13
5.3 Process Design and Flowchart 18
5.4 Wow Factor in Solution 18

viii
Chapter 6 Implementation 19
6.1 Code Description 19
6.2 Result & Outcomes 20
6.3 Video Demo Link 20
6.4 Result Analysis 20
6.5 Screenshots from the Application 20
Chapter 7 Conclusion 24
7.1 Achievements 24
7.2 Future Prospective 24
References 25
Appendix 1 – NOC Letter 26

ix
 Overview of the Company

CHAPTER 1

Objectives of the Internship Training on Pneumonia Detection from

Chest X-Ray Images Using Transfer Learning

1. Understanding Medical Imaging and Pneumonia:

o Learn the fundamentals of medical imaging and the significance of chest X-
rays in diagnosing pneumonia.
o Understand the clinical symptoms and radiological characteristics of
pneumonia.
2. Data Acquisition and Preprocessing:
o Acquire and preprocess chest X-ray datasets, ensuring data quality and
consistency.
o Learn techniques for image augmentation to enhance the diversity of the
training data.
3. Introduction to Transfer Learning:
o Understand the concept of transfer learning and its advantages in medical
image analysis.
o Familiarize with pre-trained convolutional neural networks (CNNs) such as
VGG16, ResNet, Inception, and others.
4. Model Selection and Implementation:
o Select appropriate pre-trained models for pneumonia detection.
o Implement the selected models using popular deep learning frameworks
like TensorFlow or PyTorch.
5. Model Training and Fine-Tuning:
o Train the models on the preprocessed chest X-ray images.
o Fine-tune the models to improve their performance on pneumonia detection.
6. Performance Evaluation:
o Evaluate the models using appropriate metrics such as accuracy, precision,
recall, F1-score, and AUC-ROC.
o Compare the performance of different models and fine-tuning strategies.
7. Optimization and Regularization:
o Apply optimization techniques to enhance model performance.
o Implement regularization methods to prevent overfitting and ensure
generalizability.
8. Deployment and Integration:
o Learn about model deployment in a clinical setting.
o Understand the integration of the trained model into healthcare systems for
real-time pneumonia detection.
9. Ethical and Legal Considerations:
o Discuss the ethical implications of using AI in healthcare.
o Understand the legal and regulatory requirements for deploying AI models
in clinical practice.
10.Documentation and Reporting:
o Document the entire process from data acquisition to model deployment.
o Prepare a comprehensive report summarizing the objectives,
methodologies, results, and conclusions of the project.
11.Collaboration and Communication:
o Collaborate with healthcare professionals to understand their requirements
and feedback.
o Communicate findings effectively through presentations and reports.
12.Future Directions and Research Opportunities:
o Explore future research opportunities in the field of medical image analysis
and AI in healthcare.
o Identify potential improvements and extensions of the current project.
 Overview of the Department

CHAPTER 2 OVERVIEW OF THE DEPARTMENT

2.1 It includes the details about the work being carried out in each department.
The work being carried out in each department at the GTU – Software Technology and Emerging
Group
Artificial Intelligence (AI) and Machine Learning (ML)
This department focuses on cutting-edge research and practical applications of AI and ML.
Projects may involve creating intelligent chatbots, recommendation systems, and image
recognition algorithms.
Internet of Things (IoT)
The IoT department explores the interconnected world of devices, sensors, and data. Projects
could include building smart sensors, developing energy-efficient protocols, and analyzing
real-time data streams.
Data Science
Data science is all about extracting valuable insights from large datasets. Projects might
involve analyzing customer behavior, predicting stock market trends, or optimizing supply
chain logistics.
SAP ABAP Programming
ABAP (Advanced Business Application Programming) is a programming language used in
SAP systems. Projects could include creating custom modules, integrating external systems,
and improving business processes.

2.2 List the technical specifications of major equipment used in each

department.
Internet of Things (IoT)
Raspberry Pi Clusters
These clusters consist of Raspberry Pi boards for IoT
prototyping. Specifications (per board)
CPU Broadcom BCM2711 (Quad-core ARM Cortex-A72)
RAM 4 GB LPDDR4
Storage MicroSD card
Connectivity Wi-Fi, Bluetooth

3
 Overview of the Department

Arduino Kits
Arduino boards for sensor interfacing and basic IoT projects.
Specifications
Microcontroller ATmega328P
Digital I/O Pins 14
Analog Input Pins 6
Connectivity USB, UART
Data Science
Data Servers
These servers store and manage large datasets.
Specifications
CPU Dual Intel Xeon processors
Storage RAID-configured HDDs (Hard Disk Drives)
Database PostgreSQL, MySQL
Jupyter Notebooks
Used for data exploration and
analysis. Specifications
CPU Intel Core i7 or AMD Ryzen
RAM 32 GB
Storage SSD
SAP ABAP Programming
SAP Development Servers
These servers run the SAP NetWeaver ABAP
stack. Specifications
CPU Intel Xeon
RAM 128 GB
Storage SAN (Storage Area Network)
Database SAP HANA

2.3 Explain in detail about each stage of production.

The details of each stage of production, covering various aspects from conception to the
final product

4
 Overview of the Department

Conceptualization and Ideation

This stage involves brainstorming and defining the purpose of the product. It includes
identifying the problem the product aims to solve, understanding user needs, and
envisioning the desired outcome.
Design and Planning
Design lays the foundation for the product’s functionality, aesthetics, and user experience.

Planning ensures efficient execution. Create wireframes, mockups, and prototypes.

Define user flows and interactions. Define architecture, components, and technologies
.

Create a development roadmap. Development and Implementation

This stage involves turning design concepts into a functional product. Connect front-end and back-
end components. Develop server-side logic (using languages like Python, Java, or Node.js).Set up
databases (SQL, NoSQL). Implement APIs and third-party services.

Testing and Quality Assurance (QA)

Ensure the product meets requirements, functions correctly, and is free of defects. Test, verify &
Validate the components. Involve users to test real-world scenarios. Deployment and Release

Make the product available to users. Deploy the product to a staging server for final testing.
Deploy the product to the live server. Monitor performance, security, and scalability. Address any
issues promptly.

5
 Introduction to Project

CHAPTER 3 INTRODUCTION TO PROJECT

3.1 Summary of Project

At the intersection of medical diagnostics and cutting-edge technology, the X-
RayPneumonia Detection GUI project stands as a testament to innovation in healthcare.
With a visionary approach, this project harnesses the formidable potential of Convolutional
Neural Networks (CNNs) to revolutionize the detection of pneumonia in X-ray images.
Imbued with sophistication, it strives to streamline and fortify the diagnostic process through
the seamless integration of artificial intelligence and user-friendly graphical interface
elements. In its core essence, this venture is an ode to efficiency and accessibility. The
amalgamation of Python, Kivy, and the intricate prowess of CNNs births an application that
promises not just rapid, but remarkably dependable results in discerning the presence of
pneumonia within X-ray imagery. By wielding the power of intuitive design and robust
computational models, it aims to augment the abilities of medical professionals and amplify
diagnostic precision to new heights.

3.2 Purpose
Early Detection Early detection of pneumonia is crucial for timely treatment and
management, especially in vulnerable populations such as children and the elderly. CNNs
can analyze X-ray images quickly and accurately, potentially identifying pneumonia at an
earlier stage than traditional methods.
Automation and Efficiency Automating the detection process through deep learning
algorithms reduces the need for manual interpretation of X-ray images by radiologists. This
can significantly increase the efficiency of diagnosis, especially in settings where access to
radiologists is limited.
Improving Patient Outcomes By enabling earlier detection and more accurate diagnoses,
CNN-based pneumonia detection systems have the potential to improve patient outcomes.
Timely identification of pneumonia can lead to prompt initiation of treatment, reducing the
risk of complications and improving overall prognosis.

3.3 Objective
Unified Classification Through the utilization of Convolutional Neural Networks, the
project endeavors to classify X-ray images into 'Normal' and 'Pneumonia' categories,
encapsulating both bacterial and viral pneumonia cases. This unified classification
6
 Introduction to Project

framework simplifies the diagnostic process, aiding in swift and accurate identifications for
medical practitioners.
Enhanced User Experience An inherent aspect of this project involves the development
of a user-friendly GUI that encapsulates the intricacies of pneumonia detection while
ensuring a seamless and enriching experience for medical professionals. By leveraging
intuitive design elements and straightforward functionalities, the application promises to
simplify complex diagnostic procedures.
Continuous Improvement and Community Engagement Beyond its current state, this
project stands as an evolving endeavor. It invites community engagement, feedback, and
contributions, fostering an environment of constant improvement. Bug reporting, issue
tracking, and active collaboration are encouraged, fueling the drive toward enhancing the
application's efficacy and user experience.
Intersection of Technology and Medicine At its core, the project represents a
convergence of technology and medicine, leveraging the capabilities of deep learning and
graphical interface design to bridge the gap between innovation and healthcare. It strives to
not only deliver diagnostic results but also contribute to the evolution of medical diagnostics
through modern technological advancements.

3.4 Scope
Research and Development There is ongoing research and development in improving the
performance and robustness of CNN models for pneumonia detection. This includes
optimizing network architectures, exploring novel training techniques, and investigating
ways to enhance the interpretability of model predictions.
Education and Training CNN-based pneumonia detection can also be integrated into
medical education and training programs for radiologists, physicians, and other healthcare
professionals. By providing access to annotated datasets and training resources, these
systems can help trainees develop the skills necessary for accurate interpretation of X-ray
images.
Continuous Improvement Continuous monitoring and evaluation of CNN-based
pneumonia detection systems are essential to ensure their ongoing effectiveness and
reliability. This includes monitoring performance metrics, gathering user feedback, and
incorporating updates and improvements based on evolving clinical needs and technological
advancements.

7
 Introduction to Project

3.5 Technology and Literature Review

Kivy Framework Kivy is a Python library for building cross-platform GUI applications,
offering tools for UI design and user interaction.
Deep Learning Model Integration Literature on integrating deep learning models with
Python applications provides guidance on loading, running, and interpreting model
predictions.
Python Programming Proficiency in Python programming is necessary for frontend
development, utilizing libraries like Kivy for UI design.
Documentation and Best Practices Comprehensive documentation and adherence to best
practices guides facilitate efficient development and troubleshooting.
Integration with Backend Integration of the frontend with the backend, where deep
learning models are deployed, ensures seamless operation of the application.

3.6 Project Planning

Define Objectives and Scope Clearly outline the project objectives, such as developing a
CNN-based system for pneumonia detection from X-ray images.
Gather Requirements Identify the technical requirements, including hardware (e.g., GPUs
for training), software (e.g., Python libraries, frameworks), and data requirements (e.g.,
annotated X-ray datasets).
Data Collection and Preprocessing Acquire X-ray image datasets containing both
pneumonia-positive and pneumonia-negative cases. Preprocess the data, including resizing
images, normalizing pixel values, and possibly augmenting the dataset to improve model
generalization.
Model Development Design the architecture of the CNN model for pneumonia detection,
considering factors like model depth, convolutional layer configurations, and activation
functions.Implement the model using deep learning frameworks like TensorFlow, PyTorch,
or Keras.
Evaluation and Validation Evaluate the trained model using separate validation datasets to
assess its performance in detecting pneumonia accurately. Validate the model's performance
against clinical standards and guidelines to ensure clinical relevance and accuracy.
GUI Development Design the graphical user interface (GUI) for the application using
tools like Kivy, considering usability, layout design, and user interaction patterns.Integrate
the GUI with the deep learning model for seamless interaction and real-time feedback during
the detection.

8
 System Analysis

CHAPTER 4 SYSTEM ANALYSIS

4.1 Study of Current System

The current system for X-Ray pneumonia detection relies on deep learning through CNNs,
aiming to accurately identify pneumonia patterns in X-ray images. Evaluation of its
performance demonstrates promising results, showcasing high accuracy and efficiency
compared to traditional methods. However, challenges such as generalization across diverse
datasets and patient demographics, as well as interpretability of the model's decisions,
persist. Clinical validation studies are essential to ensure the system's effectiveness in real-
world scenarios, comparing its performance to that of radiologists and assessing its impact
on clinical decision-making. Addressing limitations such as dataset biases and
standardization issues in image acquisition and labeling is crucial for further improving the
system's reliability. Future research aims to enhance model interpretability, incorporate
additional data modalities, and navigate ethical and regulatory considerations for widespread
clinical deployment.

4.2 Algorithms & Techniques

Convolutional Neural Networks (CNNs) CNNs are the cornerstone of deep learning in
image-related tasks. They consist of multiple layers, including convolutional layers, pooling
layers, and fully connected layers. CNNs automatically learn hierarchical features from
images, making them ideal for image classification tasks like pneumonia detection.
Image Preprocessing Preprocessing is crucial for enhancing the quality of input images
and improving the performance of the model. Common preprocessing techniques include
resizing images to a uniform size, normalization to standardize pixel values, and
augmentation to increase the diversity of training data (e.g., rotation, flipping, zooming).
Data Augmentation Data augmentation techniques such as rotation, translation, scaling,
and flipping are used to artificially increase the size and diversity of the training dataset.
This helps prevent overfitting and improves the generalization ability of the model.
Ensemble Learning Ensemble methods combine predictions from multiple models to
improve performance and robustness. Ensemble techniques can involve combining
predictions from different CNN architectures, training models with different initializations,
or using different data augmentation strategies.

9
 System Analysis

4.3 Dependencies
The dependencies for the XRay-Pneumonia-Detection-GUI project involve a set of essential
libraries and frameworks required to run the project successfully. These dependencies
facilitate the development, training, and deployment of the deep learning model and the
graphical user interface (GUI) application. Here are the core dependencies
Python (programming language)
Python serves as the primary programming language used in this project.
Pip (package manager for Python)
Pip is utilized for installing and managing Python libraries and packages.
TensorFlow
TensorFlow, an open-source machine learning framework, is a fundamental
component used for training the X-ray pneumonia classification model.
NumPy
NumPy is a library for numerical computing in Python. It is used for efficient
handling of arrays and data manipulation.
OpenCV (Open Source Computer Vision Library)
OpenCV is a popular computer vision library employed for various image processing
and analysis tasks.
Matplotlib
Matplotlib is a plotting library used for generating visualizations from data,
potentially used for model performance visualization in this project.
Kivy
Kivy is an open-source Python library used for developing crossplatform graphical
user interface (GUI) applications.
KivyMD
KivyMD is a library that provides Material Design components specifically designed
for Kivy applications.

4.4 Features of Proposed System

Automated detection CNNs can automatically analyze X-ray images and detect signs of
pneumonia without human intervention.
High accuracy Deep learning models trained on large datasets can achieve high accuracy
in identifying pneumonia patterns in X-ray images.
10
 System Analysis

Speed CNN-based algorithms can process X-ray images quickly, enabling rapid
diagnosis and treatment decisions.
Scalability Deep learning models can be scaled to handle large volumes of X-ray images,
making them suitable for use in healthcare settings with high patient throughput.
Adaptability Deep learning models can be adapted and fine-tuned for specific patient
populations or imaging modalities, enhancing their applicability across different healthcare
settings.
Continuous improvement As more data becomes available and algorithms are refined,
CNN-based pneumonia detection systems can continuously improve their performance and
accuracy over time.

11
 System Design

CHAPTER 5 SYSTEM DESIGN

5.1 System Design and Methodology

The model utilized in the XRay-Pneumonia-Detection-GUI project is a Convolutional
Neural Network (CNN) designed specifically for classifying X-ray images into two distinct
categories 'Normal' and 'Pneumonia'. This model was structured to perform a binary
classification task to identify cases of pneumonia in X-ray images.
Model Training

Fig 5.1 Model Training

The model undergoes a training process using a dataset segregated into training, testing, and
validation sets. Data augmentation techniques, such as horizontal flipping, might have been
applied to augment the training data, potentially enhancing the model's ability to generalize.

Model Evaluation Metrics

The model's performance is evaluated based on several evaluation metrics, including loss
and accuracy, computed across the training, testing, and validation datasets.

12
 System Design

• Train-loss 0.13, Train-accuracy 0.95

• Test-loss 0.45, Test-accuracy 0.83
• Val-loss 0.15, Val-accuracy 0.93

Fig 5.2 Building a Confusion Matrix

Fig 5.3 Confusion Matrix

13
 System Design

Performance Analysis
Based on the evaluation metrics, the model demonstrates superior performance in predicting
the 'PNEUMONIA' class (94% accuracy) compared to the 'NORMAL' class (72.2%
accuracy). However, it is important to note that the model's performance can be further
enhanced through optimizations and improvements.This comprehensive model architecture
aims to accurately identify pneumonia cases in X-ray images, providing a valuable tool for
medical professionals in automating and expediting the pneumonia diagnosis process.

Fig 5.4 Performance Analysis

5.2 Datasets
The dataset used in the XRay-Pneumonia-Detection-GUI project is a critical component in
training and evaluating the machine learning model for pneumonia detection in X-ray
images. Let's delve into the details of the datasets utilized in this project.
Datasets Used Chest X-Ray Images (Pneumonia) from Kaggle.
Dataset Link Kaggle Dataset Link

14
 System Design

Description This dataset contains chest X-ray images collected from various
sources, including pediatric and adult patients. It includes images categorized into
two classes "Normal" and "Pneumonia." The "Pneumonia" class encompasses X-
ray images depicting both bacterial and viral pneumonia cases. The dataset has a
substantial number of X-ray images belonging to each category, enabling robust
training and validation of the model.
In this model a dataset from Kaggle was used. It contained 5863 Chest X-Ray
Images in Jpeg format and the images were categorized into a) Train b) Test and c)
Validate categories. These were further broken into 2 categories, Pneumonia and
Normal. The dataset covers both Normal X-Ray images and X-Ray images with
Pneumonia.

Fig 5.5 Datasets

 System Design

Fig 5.6 Data Preprocessing

Fig 5.7 Visualize Dataset

16
 System Design

5.3 Process Design and Flowchart

Fig 5.8 Flowchart

5.4 Wow Factor in Solution

Automated Pneumonia Detection
The project achieved automation in pneumonia detection by utilizing deep learning
techniques, enabling efficient and rapid classification of X-ray images.
User-Friendly Interface
The user-friendly GUI simplified the process of uploading X-ray images, providing
intuitive functionalities for medical professionals to obtain quick and reliable results.
Model Performance
The developed CNN model showcased promising performance, accurately
classifying pneumonia cases with high accuracy, thus contributing to early and
accurate diagnoses.

17
 Implementation

CHAPTER 6 IMPLEMENTATION

6.1 Code Description

Here's an overview of the application's code structure and functionalities The application
code for the XRay-Pneumonia-Detection-GUI project typically encompasses several files
and functionalities that work collectively to enable the automation of pneumonia detection in
X-ray images. Here's an overview of the application's code structure and functionalities

File Structure
The application code is organized into various files and folders, each serving a specific
purpose
Main Python Script (main-GUI.py)
This script initiates the application and handles the primary functionalities of the
GUI.
It might include imports, configuration settings, and the main application class.
Kivy Language File (design.kv)
This file defines the graphical layout and design of the application using the Kivy
language.
It contains instructions for creating widgets, defining their properties, positioning
elements, and managing the GUI's appearance.
Model File (model-N/)
Directory containing the pre-trained deep learning model and its related files required
for inference.
Includes the architecture, weights, and other necessary components of the trained
neural network model.
Configuration File (config.json)
JSON file storing various configuration parameters used by the application, such as
model paths, window settings, and performance metrics.
Assets and Resources (Images, etc.)
Additional files or resources necessary for the application's functionalities, such as
images for the interface, icons, or any other required media.

18
 Implementation

6.2 Result & Outcomes

Launching the Application
Navigate to the project directory containing the main application file. Configure the
'config.json' file as needed (e.g., model file path, window dimensions, theme). Run
the 'main-GUI.py' file using the command python main-GUI.py (for Windows) or
python3 main-GUI.py (for Linux/Mac).
Using the GUI
Once the application window loads, you'll encounter an intuitive interface with
options to drag and drop an X-ray image for analysis. Drag and drop an X-ray image
onto the window to initiate the analysis process.
Result Interpretation
The application swiftly processes the uploaded X-ray image using the trained model.
Results, such as the classification (Normal/Pneumonia) and probability scores, are
displayed on the interface. Users can opt to export the analyzed image with the
classification results.

6.4 Result Analysis

Taking X-ray images as input and analyze the image using CNN gives output images with
pneumonia or normal caption below them. Caption pneumonia indicates the person is having
pneumonia and normal indicates the person is not suffering from pneumonia. The model
provides the results with an accuracy of 94%.

6.5 Screenshots from the Application

Fig 6.1 Command for launching the application

19
 Implementation

Fig 6.2 Loading Page

Fig 6.3 Drag & Drop Page

20
 Implementation

Fig 6.4 Prediction Pneumonia Detected

Fig 6.5 Prediction Normal Detected

21
 Implementation

Fig 6.6 Model Information

Fig 6.7 Exported Image

22
 Conclusion

CHAPTER 7 CONCLUSION

The development and implementation of the XRay-Pneumonia-Detection-GUI project aimed

to provide a user-friendly solution for automating the detection of pneumonia in X-ray
images. Our model demonstrates superior performance in predicting the 'PNEUMONIA'
class (94% accuracy) compared to the 'NORMAL' class (72.2% accuracy).The main aim of
the planned resolution is to implement the model having the best accuracy and bottom loss
error for Pneumonia Detection. The ultimate goal is to create a reliable and efficient tool for
early and accurate pneumonia detection, aiding healthcare professionals in timely diagnosis
and treatment decision-making.

7.1 Achievements
Automated Pneumonia Detection The project achieved automation in pneumonia
detection by utilizing deep learning techniques, enabling efficient and rapid classification of
X-ray images.
User-Friendly Interface The user-friendly GUI simplified the process of uploading X-ray
images, providing intuitive functionalities for medical professionals to obtain quick and
reliable results.
Model Performance The developed CNN model showcased promising performance,
accurately classifying pneumonia cases with high accuracy, thus contributing to early and
accurate diagnoses.

7.2 Future Perspective

Dataset Expansion The project could benefit from incorporating larger and more diverse
datasets, encompassing varied demographics and pneumonia presentations, to enhance the
model's robustness.
Model Refinement Continuous refinement and fine-tuning of the deep learning model,
incorporating transfer learning, ensembling, or other advanced techniques, can further
improve its performance and generalization capabilities.
Collaboration and Validation Collaborating with medical professionals to validate and
refine the model's predictions through clinical trials or expert reviews would add credibility
and real-world applicability.

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 References

REFERENCES

1. Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.

Lung pattern classification for interstitial lung diseases using a deep convolutional
neural network. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)

2. Bijaya Kumar Hatuwal, Himal Chand Thapa,2020., proposed Lung Cancer

Detection Using Convolutional Neural Network on Histopathological Images.

3. Dimpy Varshini, Kartik Thakral, Lucky Agarwal and Ankush Mital,2011.,

“Pneumonia Detection Using CNN based Feature Extraction “proceedings of 978-
5386-8158- 9,” IEEE.

4. Glozman, T., Liba, O. Hidden Cues Deep Learning for Alzheimer’s Disease
Classification CS331B project final report (2016)

5. Kumar, A., Sangwan, S.R., Arora, A., Nayyar, A., Abdel-Basset, M. Sarcasm
detection using soft attention-based bidirectional long short-term memory model
with convolution network. IEEE Access 7, 23319–23328 (2019)

6. Neuman M., Lee E., Bixby S., Diperna S., Hellinger J., Markowitz R., et al.
Variability in the interpretation of chest radiographs for the diagnosis of
pneumonia in children. Journal Of Hospital Medicine. 7, 294–298 (2012) pmid
22009855

7. Stephen O., Sain M., Maduh U. & Jeong D. An efficient deep learning approach
to pneumonia classification in healthcare. Journal Of Healthcare Engineering.
2019 (2019) pmid 31049186

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 Apendix
Code-
RESEARCH GAPS

1. Model Architecture: While both studies propose advanced deep learning models for chest X-
ray image classification, there is a research gap in exploring alternative architectures beyond
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Investigating newer
architectures like graph convolutional networks or attention mechanisms could provide further
improvements in accuracy and efficiency.

2. Dataset Diversity: The first study focuses on a benchmark dataset containing four classes:
Normal, COVID-19, Pneumonia-Viral, and Pneumonia-Bacterial. However, there is a research
gap in evaluating model performance on more diverse datasets with additional classes or
variations in demographics. Incorporating datasets from different regions or populations could
reveal potential biases or variations in model performance.

3. Interpretability: Both studies highlight the importance of accurate chest X-ray classification for
medical diagnoses. However, there is a research gap in addressing the interpretability of the deep
learning models. Exploring techniques for model explainability and providing insights into the
features driving classification decisions could enhance clinical trust and the adoption of these
models.

Clinical Validation: While the studies demonstrate promising results in terms of accuracy and efficiency, there is
a research gap in conducting rigorous clinical validation of the proposed models