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This Breast cancer is one of the most prevalent lumps in women increased day by day around in worldwide. The scheme for the detection of breast cancer is Mammographic technique that is used at the very earlier stage. In this paper two kinds of classification Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) are used to analyze the mammographic images. The two classification methods are using the image pre-processing in wavelet decomposition and image enhancement. The results are verified with 322 mammogram images which is size for 1024×1024 with PGM format. The results show that the proposed algorithm can able to classify the images with a good performance rate of 98%. It can be concluded that supervised learning algorithm gives fast and accurate classification and it works as efficient tool for classification of breast cancer cells. Keywords-Breast cancer, Mammographic technique, Support vector machines, Linear discriminant analysis, 2 dimensional discrete wavelet transform.I.

Detection of cancer is the utmost fascinating analysis space for scientists in the early period. The projected method is meant to identify cancer in the beginning phase. The projected method comprises several phases, such as image acquisition, pre-processing, segmentation, feature extraction, and classification. In our proposed work, segmentation is done to fragment the CT image. We use solid feature extraction (GLCM) technique to extract certain essential features from the segmented images. Further extracted features are considered for classification (Multi SVM) process to check whether cancerous or non-cancerous.