A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images

Abstract

Breast cancer is one of the most common and dangerous cancers in women, while it can also afflict men. Breast cancer treatment and detection are greatly aided by the use of histopathological images since they contain sufficient phenotypic data. A Deep Neural Network (DNN) is commonly employed to improve accuracy and breast cancer detection. In our research, we have analyzed pre-trained deep transfer learning models such as ResNet50, ResNetl01, VGG16, and VGG19 for detecting breast cancer using the 2453 histopathology images dataset. Images in the dataset were separated into two categories: those with invasive ductal carcinoma (IDC) and those without IDC. After analyzing the transfer learning model, we found that ResNet50 outperformed other models, achieving accuracy rates of 90.2%, Area under Curve (AUC) rates of 90.0%, recall rates of 94.7%, and a marginal loss of 3.5%.