Abstract
The success for treatment of breast cancer patients depends on the early detection of breast cancer. In this paper, computer aided system for the detection and classification of breast cancer using mammogram images. The proposed system consists of the following three stages as mammogram image enhancement, feature extraction and Classification. The Shift invariant non sub sampled Contourlet transform is used for mammogram image enhancement. The transform coefficients are extracted as features for both training and classification of mammogram images. The mammogram images classification are performed using Support vector machine (SVM) and feed forward back propagation neural network classifier. The neural network classifier achieved 100% classification rate over the images in publicly available dataset. The proposed method achieved 83% of sensitivity, 99% of specificity and 98% of accuracy in Mammogram Image Analysis Society (MIAS) dataset.
Keywords: Breast cancer, enhancement, mammograms, screening.
Graphical Abstract
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