Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
Lung Cancer Detection Using Image Processing Technique
Ms.SHIVA PARVATHI1, HARSHAVARDHINI BOTLA2, PRANITHA3, DIVYA K4,
LEKHANA G5
1
Assistant Professor, Department of ECE Malla Reddy Engineering College for Women (UGC-Autonomous)
Maisammaguda,Hyderabad-500100
2,3,4,5
UG Students, Department of ECE Malla Reddy Engineering College for Women (UGC-Autonomous)
Maisammaguda,Hyderabad-500100
To Cite this Article
Swarnima Singh and Sunita Mishra, “Empower Teenagers and Farmers about Food Preservation and
Processing Technique” Journal of Science and Technology, Vol. 08, Issue 07,-July 2023, pp25-38
Article Info
Received: 13-07-2023 Revised: 15-07-2023 Accepted: 20-07-2023 Published: 24-07-2023
ABSTRACT:
Cancer is a quite common and dangerous disease. The various methods of
cancer exist in the worldwide. Lung cancer is the most typical variety of cancer.
The beginning of treatment is started by diagnosing CT scan. The risk of death can
be minimized by detecting the cancer very early. The cancer is diagnosed by
computed tomography machine to process further. In this paper, the lung nodules
are differentiated using the input CT images. The lung cancer nodules are
classified using support vector machine classifier and the proposed method
convolutional neural network classifier. Training and predictions using those
classifiers are done. The Nodules which are grown in the lung cancer are tested as
normal and tumor image. The testing of the CT images are done using SVM and
CNN classifier. Deep learning is always given prominent place for the
classification process in present years. Especially this type of learning is used in
Published by: Longman Publishers www.jst.org.in
Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
the execution of tensor Flow and convolutional neural network method using
different deep learning libraries.
Key terms: CT image, Convolutional neural network, SVM.
1. INTRODUCTION
Lung cancer is recognized as the main reason behind the death caused due to
cancer in the worldwide. And it is not easy to identify the cancer in its early stages
since the symptoms don’t emerge in the initial stages. It causes the mortality rate
considered to be the highest among all other methods of cancer. The number of
humans dies because of the dangerous lung cancer than other methods of cancer
such as breast, colon, and prostate cancers. There exists enormous evidence
indicating that the early detection of lung cancer will minimize mortality rate.
Biomedical classification is growing day by day with respect to image. In this field
deep Learning plays important role. The field of medical image classification has
been attracting interest for several years. There are various strategies used to detect
diseases. Disease detection is frequently performed by observant at tomography
images. Early diagnosis must be done to detect the disease that is leading to death.
One among the tools used to diagnose the disease is computerized tomography.
Lung cancer takes a lot of victims than breast cancer, colon cancer and prostate
cancer together. This can be a result of asymptomatic development of this cancer.
The Chest computed tomography images are challenging in diagnostic imaging
Published by: Longman Publishers www.jst.org.in
Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
modality for the detection of nodules in lung cancer. Biomedical image
classification includes the analysis of image, enhancement of image and display of
images via CT scans, ultrasound, MRI. Nodules within the respiratory organ i.e.
lung are classified as cancerous and non-cancerous. Malignant patches indicate that
the affected person is cancerous, whereas benign patches indicate an affected
person as a non- cancerous patient. This can be done using various classifiers.
2 RELATED STUDY
Over the years, the demographic profile of lung cancer has modified.
However, maximum reviews are restricted by means of small numbers, brief
follow-up period, and show an inconsistent sample. A complete assessment of
changing developments over an extended length has no longer been done.
Consecutive lung cancer sufferers have been studied over a 10-yr length from
January 2008 to March 2018 at the All India Institute of Medical Sciences, New
Delhi, and applicable scientific information, and survival effects have been
analysed, Lung most cancers is the leading motive of most cancers-related demise
in the world and possibly to remain so in the foreseeable future. According to the
GLOBACON record 2018, lung most cancers affected approximately 2.1 million
and triggered 1.8 million deaths.[2] Cigarette smoking is with the aid of far the
most important hazard aspect for lung most cancers. Risk increases with each
amount and period of smoking.
EXISTING SYSTEM
Support Vector Machines is a method of machine learning approach taken
for classifying the system. It examines and identifies the classes using the data. It is
broadly used in medical field for diagnosing the disease. A support-vector machine
Published by: Longman Publishers www.jst.org.in
Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
builds a hyper plane in a very high or infinite dimensional area, which can be
utilized for classification, regression, or totally different operation like outliers
detection. Based on a good separation is obtained by the hyper plane in the SVM.
After classification if the gap is large to the nearest training-data pictures of any
class referred as functional margin, considering that in generally the larger the
margin, the lesser the generalization error of the classifier. Fig-1 shows the support
vector machine classifier that constructs a maximum margin decision hyper plane
to separate two different categories. Support Vector Machine is a linear model
applied for the classification and regression issues.
Fig.1. Training and prediction using SVM.
SVM algorithm finds the points closest to the line from both. The classes of these
points are referred as support vectors. The mixed data of tumor nodules and normal
nodules are provided as input In SVM algorithm the input images given are trained
and the results are predicted, tuning the various parameters. Fig shows the training
and prediction using SVM. Input images undergo feature extraction. At the training
the various SVM parameters are tuned, and then the predictions are made using the
hyper plane of SVM.
3. PROPOSED SYSTEM:
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Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
Convolutional neural networks encompass of multiple layers in its structures. CNN
could be feed forward and extremely tremendous approach especially in detection.
Network structure is built easy; has less training parameters. A convolution neural
network have multiple layers within the neural network, that consists of one or a
lot of convolution layers and so succeeded by one or more fully connected layers
as in a standard multiple layers in neural network. Convolution neural network
architecture is typically employed collaboration with the convolution layer and
pool layer. The pooling layer is seen between convolution layers. It confuses the
features of the particular position. Since not all the location features are not
important, it just needs other features and the position. The pooling layer operation
consists of max pooling and means pooling. Mean pooling calculates the average
neighbourhood inside the feature points, and max pooling calculates the
neighbourhood inside a maximum of feature points.
A CNN uses the learned features with input and make use of 2D convolutional
layers. This implies that this type of network is best for processing 2D images.
Compared to other methods of image classification, the network uses very little
pre-processing. This means that they can use the filters that have to be built by user
in other algorithms. CNNs can be utilized in various applications from image and
video recognition, image classification, and recommender systems to natural
language processing and medical image analysis.
1. Input: This layer have the raw pixel values of image.
2. Convolutional Layer: This layer gets the results of the neuron layer that is
connected to the input regions. We define the number of filters to be used in this
layer. Each filters that slider over the input data and gets the pixel element with the
utmost intensity as the output.
Published by: Longman Publishers www.jst.org.in
Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
3. Rectified Linear Unit [ReLU] Layer: This layer applies an element wise
activation function on the image data. We know that a CNN uses back propagation.
Thus in order to retain the equivalent values of the pixels and not being modified
by the back propagation, we apply the ReLU function.
4. Pooling Layer: This layer performs a down-sampling operation along the spatial
dimensions are width and height, resulting in volume.
5. Fully Connected Layer: This layer is used to compute the score classes i.e.
which class has the maximum score corresponding to the input digits.
RESULTS DESCRIPTION
The dataset used in this paper is a collection of CT images of the carcinoma
affected persons and also normal persons. Those images are of DICOM format,
every individual image is having a multiple axial slices of the chest cavity. Those
slices are displayed in the 2d form of slices. All the medical images are stored in
microdicom format. The input image of dicom format is transformed by converting
to .png, bmp and jpg format. The pydicom package which is available for spyder
environment is used. The python language works good with all the dicom format
images.
Published by: Longman Publishers www.jst.org.in
Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
Fig.2. Lung cancer CT scans (a) Input image, (b) Median filtered image, (c) Nodules
representation, (d) Detection of nodule as normal nodules
At valuation several metrics are utilised. Using confusion matrix, the performance
is calculated. The binary classification technique is also realized. Confusion matrix
is the easily understandable metrics used to find the model's accuracy. The
accuracy of the system is determined by looking at the TN, TP, FN, and FP. The
Results for the SVM classifiers are shown as various parameters like confusion
matrix, accuracy score, and reports are extracted. Then followed by receiver
operating characteristic curve is obtained.
4. CONCLUSION:
This study draws attention to the diagnosis of lung cancer. Lung nodule
classification is benign and malignant. The proposed method CNN architecture is
specially regarded for its success in image classification compared to support
vector machine. For biomedical image classification operation, it also obtains
Published by: Longman Publishers www.jst.org.in
Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
successful results. CNN architecture is used for classification in the study.
Experimental results show that the proposed method is better than the support
vector machine in terms of various parameters. The images in the data set used are
rather small. In the future, the performance of the system can be improved with a
larger dataset and an improved architecture. The proposed system is able to detect
both benign and malignant tumors more correctly.
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Journal of Science and Technology
ISSN: 2456-5660 Volume 8, Issue 07 (July -2023)
www.jst.org.in DOI:https://doi.org/10.46243/jst.2023.v8.i07.pp38 - 47
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