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In this study, a computer aided breast cancer diagnosis framework to group masses in the Mammography Image Analysis Society (MIAS, 2012) database mammogram images utilizing Improved Square Centroid Lines Gray level distribution Method (ISCLGM) is presented. Breast cancer is the heading reason for non-preventable cancer passing among women. Early discovery of the cancer can lessen death rate. Studies have demonstrated that radiologists can miss the identification of a noteworthy extent of anomalies not withstanding having high rates of false positives. In this study, he key peculiarities utilized for the characterization of ISCLGM is extracting the features through three dimensional magnetic resonance based Texture Detection algorithm and get classified using the Multi-resolution Diatric curvelet Transform by Gradient field analysis and they are fed into SVM classifier to classify mass/non-mass image and also benign/malignant images. This System enables the radiologists to assist the breast cancer cells more effectively through the SVM (Support Vector Machine) based Microarray feature selection technique. The proposed method provides classification accuracy of 98% and yields greater efficiency in detecting the breast cancer.

Breast Cancer Detection Using Multi-resolution Diatric Microarray Curvelet Transform