A new breast cancer diagnosis application based on ResNet50

Anjie Le, Zhanghao Li, Haoyun Tang, Haobo Yang

Abstract:

Histopathology is the primary tool employed in breast cancer diagnosis. It involves examining metastatic tissues of lymph
nodes under a microscope. Histopathologists are responsible for making tissue diagnoses, and the process is challenging
and tedious. To diminish their workload and allocate more time to efficiently maintain patients' care, deploying an
intelligent system to support the diagnosis is reasonable. Therefore, we introduced a new breast cancer diagnosis
application based on deep learning technology in this paper. The application's foremost objectives were to handle the vast
dataset of digital pathology scans and train deep residual networks to classify small patches from the sizable whole slide
images with higher accuracy. Experimental outcomes indicated that our model could achieve 97.3%. Another noteworthy
feature was a FAQ chatbot that we implemented for patient consulting.