Discriminative Feature Learning for Thorax Disease Classification in Chest X-Ray Images

This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture the intrinsic relationships among different disease features and utilize them to boost the multi-label disease recognition rates jointly. In this project, we used transfer learning technique, named Dense net, to achieve our target prediction and also used to improve the performance of thorax disease classification in CXRs. Experiments conducted on the thorax disease dataset demonstrate the effectiveness of the proposed method.

Keywords: Classification, Thorax disease, Dense Net, Transfer Learning.