A modified recurrent neural network (MRNN) model for and breast cancer classification system

Breast cancer is most dangerous cancer among women. Image processing techniques are used for Breast cancer detection. A Block-based cross diagonal texture matrix (BCDTM) method is used first to extract Haralick’s features from each mammography ROI. Likewise, wrapper method is utilized to choose the crucial features from the condensed feature vector. There are lot of factors that affects the quality of the images such as salt or pepper noise. As a result, this is less precise and more prone to mistakes because of human. In order to address the problems, input breast image is first pre-processed via median filtering to reduce noise. ROI segmentation is done using weighted K means clustering. Feature extraction, texture and form descriptors based on Centroid Distance Functions (CDF) and BCDTM are used. Kernel Principal Component Analysis (KPCA) is used as dimensionality reduction on the extracted features. Improved Cuckoo Search Optimization (ICSO) is used to compute relevant feature selection. Modified Recurrent Neural Network (MRNN) is utilized to classify breast cancer into benign and malignant. Results show that the suggested model achieved highest accuracy, precision and recall values compared with other state-of-the-art approaches.