Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas. However, it is very difficult to distinguish benign and malignant cases, especially for the small size lesions in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists. This work proposes a neural-genetic algorithm for feature selection in conjunction with neural network based classifier. It also combined the computer-extracted statistical features from the mammogram with the human-extracted features for classifying different types of small breast abnormalities. It obtained 90.5% accuracy rate for calcification cases and 87.2% for mass cases with difference feature subsets. The obtained results show that different types of breast abnormality should use different features for classification.
Available at: http://works.bepress.com/kuldeep_kumar/25/