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Recognition of either masses or tissues in a mammogram digital images is a key issue for radiologist. Present methods uses medial filter and morphological operations for detection of suspected cases in a mammogram. They use region of interest (ROI) segmentation for extraction of masses and classification of levels of severities.  Classification of large number of mammogram images based on breast cancer cases takes longer computation time for performing of ROI segmentation.  This is addressed by multi-ROI segmentation and it retrieves the textual properties of large mammogram images for effectively determining the breast cancer mammogram images.Experimental results shows the better performance of proposed method than existing ROI based texture feature extraction.

Classification is one of the important areas of research in the field of data mining and machine learning. This paper discusses about GA, ANN and SVM algorithm and their use in classification. The artificial neural network is the widely used technique for classification and prediction. ANN has some disadvantage such long learning rate, high computational cost, convergence at local optima and adjustment of weight. Optimization techniques and hybridization improve ANN performance. GA is an optimization technique that produces optimization of the problem by using natural evolution. SVM use the nonlinear kernel functions that implicitly map input data into high-dimensional feature spaces. Hybridization is a technique which combines the two or more classifier to improve the performance of the classifier.

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