Improved Residual Neural Network for Breast Cancer Classification

Breast cancer is one of the most dangerous types of cancer, especially for women. In 2015, it became the deadliest cancer after lung cancer in America. Some studies found that both self-detection and prevention are important factors in dealing with this cancer. The process of diagnosing breast cancer traditionally takes a long time. Moreover, pathologists are not 100% sure of the results of their diagnosis. Therefore, in this research, a computer-aided system is developed to help doctors to classify cell types based on histopathological images. In this research, a new model based on convolutional neural networks with an improved Residual Neural Network (ResNet) architecture is proposed to distinguish histopathological images into some classes of breast cancers. Testing on the BreakHis dataset shows that the best performance of the proposed method gives the average accuracies of 99.3% and 94.6% for binary and eight-class classifications, respectively. These results are comparable to state-of-the-art results in the recent study.