CLASSIFIERS BASED ON ARTIFICIAL INTELLIGENCE TECHNIQUES FOR THE DIAGNOSIS OF LUNG CANCER

摘要

In order to develop algorithm 39 CT scan images of patients have been considered consisting of Benign Tumor, Malignant Tumor and Normal Lung CT Scan image. With a view to extract features from the CT scan images after image processing, an algorithm is developed which proposes two-dimensional discrete cosine Transform domain coefficients in addition to Average, Standard Deviation, Entropy, Contrast, Correlation, Energy, Homogeneity. The suitability of classifiers based on Multilayer Perceptron (MLP) Neural Network is explored with the optimization of their respective parameters in view of reduction in time as well as space complexity. A separate Cross-Validation dataset is used for proper evaluation of the proposed classification algorithm with respect to important performance measures, such as MSE and classification accuracy. The Average Classification Accuracy of MLP Neural Network comprising of one hidden layers with 7 PE's organized in a typical topology is found to be superior (100 %) for Training . Finally, optimal algorithm has been developed on the basis of the best classifier performance.