A breast cancer diagnose application using deep learning technology
Qing Hao, Guankai Sang, Wenqing Zhang · Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering · 2021
Breast cancer is a cancer which has the highest morbidity in women worldwide. Modern medicine uses imaging methods to analyze pathological images, which may cause the human error, and consume resources and manpower. Deep learning can help people and doctors to detect breast cancer, treat it better. This paper developed a breast cancer diagnosis system based on the Convolutional Neural Networks (CNN). We proposed a convolutional model consists of two convolutional layers and pooling layers. The Breast Cancer Histopathology Image Classification (BreaKHis) dataset was used to contain benign and malignant two classes. BCE loss was used as the loss function during our training process. The learning rate, optimizer, training epochs are 0.001, Adam and 11, respectively. In addition, we also developed a breast cancer diagnosis application using the Flask, which the users can use to detect breast cancer online and know where has cancerous by putting red boxes. Experimental results indicate that our cancer diagnosis system can provide good feedback to breast cancer patients.