Detecting and Classifying Invasive Breast Carcinoma by the Faster R-CNN with Resnet 50
Sirui Chen, Tengfei Yu, Wen He, Yicheng Wang, Lei Qi, Zhongbao Chen · 2024
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.