Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network
Qasem Abu Al‐Haija, Adeola Adebanjo
Abstract
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.