Detecting and Classifying Invasive Breast Carcinoma by the Faster R-CNN with Resnet 50

In recent statistical data, compared with other types of cancer, breast cancer has been the most commonly diagnosed cancer in the world for females, and invasive breast carcinoma occupies a significant ratio in breast cancer. Therefore, in order to short the detection time consumption, we need a computer-aided, deep-learning-based invasive breast carcinoma detection and classification solution. The dataset includes around two thousand images with two unbalanced classes: benign and malignant. The accuracy of the Faster-RCNN with Resnet-50 in this dataset is 85.5% for malignant and 84.5% for benign. As for the patients, the mean AP is 99.9% that 99.1% in malignant patients and 99.0% in benign patients.