Transferability of Pretrained Convolutional Neural Networks for Breast Cancer Detection

Abstract

Breast cancer is the most common cancer in the world. In breast cancer, the invasive ducal carcinoma (IDC) is the most common breast cancer. For the past few years, more and more machine learning models have been used in medical treatment as an auxiliary tool for doctors. In this work, the machine learning model mainly including Convolutional Neural Network (CNN) and transfer learning was employed to identify breast cancer images to test if the histopathological breast sections are IDC. Since the process of diagnosing IDC by naked eyes is tedious, and time-costy, it could be helpful to develop a model to let machine classify the pathological section, such that the process would be more efficient. While based on previous study, it has shown that deep learning methods could achieve high performance, but it remains to be a question, that if the pre-trained models trained on datasets with no weights on medical images could be helpful while applying on tasks such as this study. The goal is to explore how transfer learning performs compare to other methods. In previous breast cancer recognition work, the SVM model did a pretty good job, so, in this work, a comparison will be done among CNN, transfer learning and SVM. In this study, methods such as cross validation, feature scaling, Support Vector Machine (SVM), CNN and transfer learning were applied to find which one can Figure out the types of pathological sections in the best way; different approaches to evaluate the performance of SVM, CNN and transfer learning model were applied, and find the best one to classify breast cancer pathological sections; the best model were selected regarding to these standards. Finally, it turns out that pre-trained model DenseNet201 for transfer learning works very well. This suggests that transfer learning can achieve good performance in this case by utilizing the weight of ImageNet.