Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay
This work addresses breast cancer diagnosis for medical applications, but it is incremental as it builds on existing transfer learning techniques.
The paper tackled breast cancer screening by developing a CAD system using a CNN with transfer learning and exponential decay fine-tuning, achieving better performance than other methods on the same dataset.
In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on a deep Convolutional Neural Network (CNN) model, to build an end-to-end learning process that classifies breast mass lesions. We investigate the impact that has transfer learning when large data is scarce, and explore the proper way to fine-tune the layers to learn features that are more specific to the new data. The proposed approach showed better performance compared to other proposals that classified the same dataset.