Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network

Breast cancer disease is the second most common world cause of cancer death in women. However, the early diagnostics and detection can provide a significant chance for correct treatment and survival. In this work, we propose an accurate and inclusive computational breast cancer diagnosis framework using ResNet-50 convolutional neural network to classify histopathological microscopy images. The proposed model employs transfer learning technique of the powerful ResNet-50 CNN pretrained on ImageNet to train and classify BreakHis dataset into benign or malignant. The simulation results showed that our proposed model achieves exceptional classification accuracy of 99% outperforming other compared models trained on the same dataset.