A Deep Convolutional Neural Network for Breast Cancer Detection in Mammograms

Breast cancer is the most prevalent ailment among women worldwide. The screening for detecting breast cancer is mammograms. The anticipated methodology uses preprocessing, feature extraction and classification. The main objective is early detection of the diseases so that the lifetime can be increased. A popular deep convolutional neural network (DCNN) model is used for mammogram classification that classifies into normal or abnormal. Initially, the mammograms are preprocessed using contrast limited adaptive histogram equalization (CLAHE). These images are fed to the DCNN model which contains five convolutional layers that use rectified linear unit (ReLU) as the activation function, and max-pooling is used to select the best features. The fully connected layer uses softmax as the activation function and classifies the selected features, and these are done with several optimizers, namely stochastic gradient descent (SGD), RMSprop, and Adam. The experiments are done on two benchmark datasets, namely the Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM), and performance is measured with sensitivity, specificity, and accuracy. The results show that DCNN with SGD optimizer gives better results compared to the traditional methods.