期刊
MEDICAL PHYSICS
卷 49, 期 1, 页码 231-243出版社
WILEY
DOI: 10.1002/mp.15328
关键词
computer-aided diagnosis; deep learning; pneumothorax; transfer learning; X-ray images
资金
- National Natural Science Foundation of China [522919591260, 81974276, 523]
The study proposes a two-stage deep learning strategy based on ResNet for pneumothorax identification, achieving state-of-the-art performance on the NIH dataset with an accuracy of 94.4%, AUC of 97.3%, and precision, recall, specificity, and F1-score all around 94%.
Purpose Pneumothorax is a life-threatening emergency that requires immediate treatment. Frontal-view chest X-ray images are typically used for pneumothorax detection in clinical practice. However, manual review of radiographs is time-consuming, labor-intensive, and highly dependent on the experience of radiologists, which may lead to misdiagnosis. Here, we aim to develop a reliable automatic classification method to assist radiologists in rapidly and accurately diagnosing pneumothorax in frontal chest radiographs. Methods A novel residual neural network (ResNet)-based two-stage deep-learning strategy is proposed for pneumothorax identification: local feature learning (LFL) followed by global multi-instance learning (GMIL). Most of the nonlesion regions in the images are removed for learning discriminative features. Two datasets are used for large-scale validation: a private dataset (27 955 frontal-view chest X-ray images) and a public dataset (the National Institutes of Health [NIH] ChestX-ray14; 112 120 frontal-view X-ray images). The model performance of the identification was evaluated using the accuracy, precision, recall, specificity, F1-score, receiver operating characteristic (ROC), and area under ROC curve (AUC). Fivefold cross-validation is conducted on the datasets, and then the mean and standard deviation of the above-mentioned metrics are calculated to assess the overall performance of the model. Results The experimental results demonstrate that the proposed learning strategy can achieve state-of-the-art performance on the NIH dataset with an accuracy, AUC, precision, recall, specificity, and F1-score of 94.4% +/- 0.7%, 97.3% +/- 0.5%, 94.2% +/- 0.3%, 94.6% +/- 1.5%, 94.2% +/- 0.4%, and 94.4% +/- 0.7%, respectively. Conclusions The experimental results demonstrate that our proposed CAD system is an efficient assistive tool in the identification of pneumothorax.
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