Computer aided detection and classification of mammogram using self-adaptive resource allocation network classifier

This study presents a computer aided system for automatic detection and classification of breast cancer in mammogram images. First the suspicious region or the Region of Interest is identified and extracted using Intuitionistic Fuzzy C-Means Clustering technique. Next multilevel Discrete Wavelet Transformation is applied to the extracted Region of Interest. After applying Discrete Wavelet Transformation, histogram features, Gray Level Concurrence wavelet features, and wavelet energy features are extracted from each Region of Interest of the image. Before classification, Principal Component Analysis is applied on the extracted features to reduce the feature dimension. Finally, the feature database is submitted to self-adaptive resource allocation network classifier for classification. The proposed system is verified with 295 mammograms in the Mammographic Image Analysis Society Database. The result shows that the proposed algorithm produces better results.