A new breast cancer diagnosis application based on ResNet50
Anjie Le, Zhenghao Li, Haoyun Tang, Haobo Yang · Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering · 2021
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.