Application of Machine Learning Techniques for the Diagnosis of Lung Cancer with ANT Colony Optimization

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cancer is the leading cause of cancer-related death in the world-wide. The prognosis is poor, with less than 15% of patients surviving 5 years after diagnosis. The poor prognosis is attributable to lack of efficient diagnostic methods for early detection and lack of successful treatment metastatic disease. However, persons with early lung cancer have lower lung cancer-related mortality than those with extensive disease, suggesting early detection and treatment of lung cancer might be beneficial. Computer Tomography is a clinical recommended imaging tool for the detection, diagnosis and follows up of many cancers. The most essential and challenging task for physicians is detection of lesions. Detection of lesions is found to be most difficult because of low contrast of an image or sometimes their residual. This paper is aimed to design computer aided diagnosis (CAD) system which has four different phases for detection of cancerous nodules from lung CT (Computer Tomography) images and they are preprocessing, feature extraction, feature selection and classification. Ant colony optimization as feature selection technique is being used to get more accurate result. Two kinds of machine learning techniques, viz., SVM (Support Vector Machine) and ANN (Artificial Neural Network) have been presented which classify the abnormal or normal lung image. ANN has proved that it gives the best accuracy as compared to SVM. The system accuracy using SVM is 93.2% and ANN is 98.40%. In this study, our methods are validated via a series of experimentation conducted with a data set of 250 lung CT images and the procedure is implemented using MATLAB tool. Keywordsaided diagnosis (CAD) system, lung nodule, thresholding, ant colony optimization (ACO), support vector machine (SVM), artificial neural network (ANN).