An integrated approach for breast cancer classification

This proposed breast cancer classification work aims to generate an automated, reliable, robust, and combined system for early breast cancer detection. In this proposed work, three transfer learning-based classifiers Binary, Benign, and Malignant are constructed. The Binary classifier classifies breast cancer as benign and malignant, the Benign classifier classifies four sub-classes of benign cancer and the Malignant classifier classifies four sub-classes of malignant cancer. All three classifiers are individually trained for their corresponding classification task and then integrated to give the outcome of the combined proposed system. As a result, the proposed system automatically classifies cancer into its major class and then sub-class with greater accuracy. The proposed breast cancer classification work is performed on BreaKHis and Breast cancer histology image (BACH) data. The classification performance of all three classifiers and the combined system is measured in terms of accuracy, recall (sensitivity), precision, and f1-score and then further compared with state-of-the-art works.