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In this paper, we introduce a method of functionally classifying lung cancer cells from normal cells by using Tetrakis Carboxy Phenyl Porphine (TCPP) and well-known computational intelligent techniques. Tetrakis Carboxy Phenyl Porphine (TCPP) is a porphyrin that is able to label cancer cells due to the increased numbers of low density lipoproteins coating the surface of cancer cells and the porous nature of the cancer cell membrane. Lung cancer is the leading cancer killer in the world. Novel early detection technologies are needed to maximize the chance for a potentially curable stage of lung cancer. When identified early (radiographic stage 1), non small cell lung carcinoma is routinely resected with survival rates of 40 to 85%. Unfortunately, most lung cancers present at an advanced stage resulting in a dismal overall 5 year survival of 15%. We study the performance of kernel methods in the context of classification accuracy on Biomoda cultured lung sputum dataset. We use a Library for support vector machines (LIBSVM) for model selection. Through a variety of comparative experiments, it is found that SVMs perform the best for detecting lung cancer. Results show that all 79 features we use give the best accuracy to identify lung cancer cells. Our results, thus, demonstrate the potential of using learning machines in detecting and classifying lung cancer cells from normal cells.
Date of Conference: 23-29 Aug. 2009