Multi-Input CNN Models for Breast Cancer Detection Using BreakHis Database

Breast cancer (BC) is one of the diseases that women suffer most, especially in the world. Routine breast checks are vital for both early diagnosis and early treatment of the person concerned. Computer aided diagnosis systems have also come a long way in being a helpful tool for pathologists during diagnosis. In this work, a novel convolutional neural network (CNN) is proposed for the effective diagnosis of BC from histopathological images. Since classical CNNs have only one input, the network is to use only the raw images from the dataset in the training process. This limits the network from using an extra feature as an input. However, the proposed model has two inputs, unlike classical CNN structures. One input of the network uses histopathological raw images and the other input uses deep features of related images. All of the experimental studies were performed on the widely used BreaKHis dataset. For the test of performance, the accuracy criterion was preferred and the 5-fold cross-validation technique was taken into account. Accuracy scores of 99.94%, 98.94%, 99.05%, and 97.30% were obtained in 40×, 100×, 200× and 400× sub-datasets, respectively. While the results obtained were highly effective values for the diagnosis of BC, they were also far superior to other results reported in the literature.