Lesion-aware convolutional neural network for chest radiograph classification

To investigate the performance of a deep-learning approach termed lesion-aware convolutional neural network (LACNN) to identify 14 different thoracic diseases on chest X-rays (CXRs).

In total, 10,738 CXRs of 3,526 patients were collected retrospectively. Of these, 1,937 CXRs of 598 patients were selected for training and optimising the lesion-detection network (LDN) of LACNN. The remaining 8,801 CXRs from 2,928 patients were used to train and test the classification network of LACNN. The discriminative performance of the deep-learning approach was compared with that obtained by the radiologists. In addition, its generalisation was validated on the independent public dataset, ChestX-ray14. The decision-making process of the model was visualised by occlusion testing, and the effect of the integration of CXRs and non-image data on model performance was also investigated. In a systematic evaluation, F1 score, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) metrics were calculated.

The model generated statistically significantly higher AUC performance compared with radiologists on atelectasis, mass, and nodule, with AUC values of 0.831 (95% confidence interval [CI]: 0.807-0.855), 0.959 (95% CI: 0.944-0.974), and 0.928 (95% CI: 0.906-0.950), respectively. For the other 11 pathologies, there were no statistically significant differences. The average time to complete each CXR classification in the testing dataset was substantially longer for the radiologists (∼35 seconds) than for the LACNN (∼0.197 seconds). In the ChestX-ray14 dataset, the present model also showed competitive performance in comparison with other state-of-the-art deep-learning approaches. Model performance was slightly improved when introducing non-image data.

The proposed LACNN achieved radiologist-level performance in identifying thoracic diseases on CXRs, and could potentially expand patient access to CXR diagnostics.