Department of Computer Science (UG)

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    BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINE AND GENETIC PROGRAMMING
    (International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), 2013-09) Menaka K; Karpagavalli S
    Breast cancer is one of the most leading causes of death among women. The early detection of abnormalities in breast enables the radiologist in diagnosing the breast cancer easily. Efficient tools in diagnosing the cancerous breast will help the medical experts in accurate diagnosis and timely treatment to the patients. In this work, experiments carried out using Wisconsin Diagnosis Breast Cancer database to classify the breast cancer either benign or malignant. Supervised learning algorithm Support Vector Machine (SVM) with kernels like Linear, Polynomial and Radial Basis Function and evolutionary algorithm Genetic Programming are used to train the models. The performance of the models are analysed where genetic programming approach provides more accuracy compared to Support Vector Machine in the classification of breast cancer and seems to be an fast and efficient method.
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    MAMMOGRAM CLASSIFICATION USING EXTREME LEARNING MACHINE AND GENETIC PROGRAMMING
    (IEEE Xplore, 2014-01-03) Menaka K; Karpagavalli S
    Mammogram is an x-ray examination of breast. It is used to detect and diagnose breast disease in women who either have breast problems such as a lump, pain or nipple discharge as well as for women who have no breast complaints. Digitized mammographic image is analysed for masses, calcifications, or areas of abnormal density that may indicate the presence of cancer. Automated systems to analyse and classify the mammogram images as benign or malignant will drive the medical experts to take timely clinical decision. In this work, the mammogram classification task carried out using powerful supervised classification techniques namely Extreme Learning Machine with kernels like linear, polynomial, radial basis function and Genetic Programming. The various task involved in this work are image preprocessing, feature extraction, building models through training and testing the classifier. The two types of mammogram image, Benign and Malignant are considered in this work and 50 images for each type collected from Mini MIAS database. Selection of Region of Interest (ROI) from the original image and Adaptive Histogram Enhancement are applied on the mammogram image before extracting the intensity histogram and gray level co-occurrence matrix features. In the dataset, for training 80% of the data are used and for testing 20% of data are used. Models are built using Extreme Learning Machine and Genetic Programming. The performances of the models are tested with test dataset and the results are compared. The predictive accuracy and training time of the classifier Genetic Programming is substantially better than the classifier built using Extreme Learning Machine with kernels linear, polynomial and radial basis function.