MFCNet: Multi-Feature Fusion Neural Network for Thoracic Disease Classification

This paper aims to automatically diagnose thoracic diseases on chest X-ray (CXR) images using convolutional neural networks (CNN). Most existing approaches typically employ a global learning strategy and use CNN with small convolutional kernels for thoracic disease classification. However, irrelevant noisy regions may affect the global learning strategy; small convolutional kernels can only capture fewer discriminant features. To address the above problems, we construct a multi-feature fusion neural network (MFCNet), which can fully use the global and weighted local features. Specifically, the global features are first generated by the global branch. Weighted local features are generated by multiplying the global feature and the heart-lung region mask identified by the Lung-heart Region Generator (LHRG). At last, the fusion branch integrates the global and weighted local features to complement the lost discriminative feature of the global branch and the local branch, thus enabling a better feature presentation for thoracic disease classification. Extensive experiments on the NIH ChestX-ray 14 dataset demonstrate that the MFCNet model achieves superior performance (average AUC=0.844) compared to state-of-the-art methods. Source code is released in https://github.com/Warrior996/MFCNet.