Abstract
Breast cancer is one of the most prominent sources of death in females. Every year many women suffer breast cancer, and, in the end, death occurs. The early detection of breast cancer may cause to reduce the death rate and save women's lives. The medical care and cost of prevention of women's breast cancer are costly and become a priority to diagnose breast cancer at its early stages. Initially, the mammography technique was the leading technique to detect the early stage of breast cancer. However, it cannot deal with a tumor size of less than 2 mm. To overcome this challenge, by considering the DMR-thermogram images, a novel layer-based Convolutional Neural Network (BreastCNN) for breast cancer detection and classification was proposed. BreastCNN method works in five different layers and uses different types of filters. The learning rate and structures of layers change after every convolution layer. The proposed technique is tested on the Database for Mastology Research (DMR) having 745 healthy and 261 sick images. The performance is calculated as the statistical values known as sensitivity, specificity, precision, accuracy, and F1-score. The proposed technique shows better accuracy of 99.7% as related to the already presented methods.