Use of Fuzzy C-Mean and Fuzzy Min-Max Neural Network in Lung Cancer Detection
Kiran S. Darne1, Suja S. Panicker2

1Kiran S. Darne, Computer Engineering, Pune University/ MIT College, Pune, India.
2Suja S. Panicker, Computer Engineering, Pune University/ MIT College, Pune, India.
Manuscript received on June 03, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 265-269 | Volume-3 Issue-3, July 2013. | Retrieval Number: C1713073313/2013©BEIESP
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Abstract: Lung cancer is a disease characterized by uncontrolled cell growth in tissues of the lung and is the most common fatal malignancy in both men and women. Early detection and treatment of lung cancer can greatly improve the survival rate of patient. Artificial Neural Network (ANN), Fuzzy C-Mean (FcM) and Fuzzy Min-Max Neural network (FMNN) are useful in medical diagnosis because of several advantages. Like ANN has fault tolerance, flexibility, non linearity, while FcM gives best result for overlapped data set, data point may belong to more then one cluster center and always converges .and , also, FMNN has advantages like online adaptation, non-linear separability, less training time, soft and hard decision. In this work, we propose to use FcM and FMNN on standard datasets, to detect lung cancer.
Keywords: Classification, Clustering, Fuzzy System, FCM, FMNN.