Lung Cancer Detection using Convolutional Neural Network
Sayana Sharma1, Mandeep Kaur2, Deepak Saini3
1Sayana Sharma, Currently Pursuing M. tech in Electronics and Communication Stream as Final year Student from Punjabi University, Patiala.
2Mandeep Kaur, Assistant, Professor and is currently pursuing her Ph. D in Punjabi university, Patiala.
3Dr. Deepak Saini, Assistant, Professor in Punjabi university, Patiala
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3256-3262 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8836088619/2019©BEIESP | DOI: 10.35940/ijeat.F8836.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy.
Keywords: CAD, CNN, GLCM, Image processing, Lung cancer, Threshold Segmentation.