CAD System for Lung Cancer and its Stages Detection using Image processing Techniques
Jenif D Souza W S1, Jothi S2, Chandrasekar A3
1Jenif D Souza W S*, Assistant professor, Department of Computer Science and Engineering, St. Joseph’s College of Engineering. OMR, Chennai, India.
2Jothi S, Associate professor, Department of Computer Science and Engineering, St. Joseph’s College of Engineering. OMR, Chennai, India.
3Chandrasekar A, Professor, Department of Computer Science and Engineering, St. Joseph’s College of Engineering. OMR, Chennai, India.
Manuscript received on January 12, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 1612-1615 | Volume-9 Issue-4, February 2020. | Retrieval Number: C8406019320/2020©BEIESP | DOI: 10.35940/ijitee.C8406.029420
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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 Lung Cancer is a most common cancer which causes of death to people. Early detection of this cancer will increase the survival rate. Usually, cancer detection is done manually by radiologists that had resulted in high rate of False Positive (FP) and False Negative (FN) test results. Currently Computed Tomography (CT) scan is used to scan the lung, which is much efficient than X-ray. In this proposed system a Computer Aided Detection (CADe) system for detecting lung cancer is used. This proposed system uses various image processing techniques to detect the lung cancer and also to classify the stages of lung cancer. Thus the rates of human errors are reduced in this system. As the result, the rate of obtaining False positive and (FP) False Negative (FN) has reduced. In this system, MATLAB have been used to process the image. Region growing algorithm is used to segment the ROI (Region of Interest). The SVM (Support Vector Machine) classifier is used to detect lung cancer and to identify the stages of lung cancer for the segmented ROI region. This proposed system produced 98.5 % accuracy when compared to other existing system.
Keywords: CAD, Region Growing, SVM, Staging, Binarization
Scope of the Article: Image Analysis and Processing