A Study on Automatic Detection of IDC Breast Cancer with Convolutional Neural Networks

Developing effective methods for the automated detection of IDC remains a challenging problem for breast cancer diagnosis. Recently, Cruz and his coworkers proposed a machine learning approach for detection of invasive ductal carcinoma (IDC) from whole image slides containing breast cancer cells. Their method, based on a Convolutional Neural Network (CNN), does not need to handcraft features from images. Their work has the potential of revolutionizing cancer detection, promoting further research and development in this exciting direction. Inspired by Cruz's work, our team investigated various CNN architectures for automated detection of breast cancer. We first implemented a baseline CNN similar to Cruz'ss, and then extended it to four different architectures. All architectures were trained over a large dataset of approximately 275,000, 50x50 RGB image patches. Measurement of quantitative results were done through a global average of 10-Fold Cross Validation tests. Each methodology used the performance measures, F-Measure (F1), and Balanced Accuracy (BAC). One of the fine-tuned CNN architectures yielded the best results in F1, BAC and accuracy (92%, 87%, and 89%, respectively). We also discovered through this study that data augmentation was not effective in automatic detection of breast cancers with the given dataset.