Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts

Automated detection of lung lesions on Chest X-ray images shows good performance to reduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well and truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper, a multi-label classification model combining attention-based neural networks and association-specific contexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional neural network and a long short-term memory network are first aligned by an attention mechanism to take advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM (CAL) network. In addition, a mining method of implicit association strength to obtain an association network of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA network provides possible clinical relationships between lesions to help the CAL network obtain better predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms some state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and F-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method may be useful in the computer-aided detection of multiple lesions on chest X-ray images.