用视觉变压器自动识别肺部疾病，准确率超95%
Automated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
- 采用视觉变压器模型分析胸部X光片，实现自动化诊断
- 二分类准确率达99%，多分类准确率为95.25%
- 适合医学影像分析与AI辅助诊断研究者参考
背景：肺部疾病是重大健康问题，尤其在儿童和老年人中常见，常由肺部感染引起，是儿童死亡的主要原因之一。全球每年有大量死亡与肺部疾病相关，因此早期准确诊断至关重要。放射影像对这类疾病的诊断具有重要价值。主要肺部疾病包括肺炎、哮喘、过敏、慢性阻塞性肺病（COPD）、支气管炎、肺气肿和肺癌，均构成重大公共卫生挑战。早期预测有助于识别风险因素并采取预防措施。方法：本研究使用来自Mendeley Data的3,475张胸部X光片数据集（Talukder, M. A., 2023），分为正常、肺部阴影和肺炎三类。对比了五种预训练深度学习模型（CNN、ResNet50、DenseNet、CheXNet、U-Net）以及两种迁移学习算法（视觉变压器ViT和移位窗口Swin）。分析涵盖二分类和多分类场景。结果：在二分类中，区分正常与病毒性肺炎；在多分类中包含所有三类。所提方法（ViT）表现优异，二分类准确率达99%，多分类准确率为95.25%。
原文摘要 · Abstract (English)
Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and is one of the leading causes of mortality in children. Globally, lung-related diseases claim many lives each year, making early and accurate diagnoses crucial. Radiographs are valuable tools for the diagnosis of such conditions. The most prevalent lung diseases, including pneumonia, asthma, allergies, chronic obstructive pulmonary disease (COPD), bronchitis, emphysema, and lung cancer, represent significant public health challenges. Early prediction of these conditions is critical, as it allows for the identification of risk factors and implementation of preventive measures to reduce the likelihood of disease onset Methods: In this study, we utilized a dataset comprising 3,475 chest X-ray images sourced from from Mendeley Data provided by Talukder, M. A. (2023) [14], categorized into three classes: normal, lung opacity, and pneumonia. We applied five pre-trained deep learning models, including CNN, ResNet50, DenseNet, CheXNet, and U-Net, as well as two transfer learning algorithms such as Vision Transformer (ViT) and Shifted Window (Swin) to classify these images. This approach aims to address diagnostic issues in lung abnormalities by reducing reliance on human intervention through automated classification systems. Our analysis was conducted in both binary and multiclass settings. Results: In the binary classification, we focused on distinguishing between normal and viral pneumonia cases, whereas in the multi-class classification, all three classes (normal, lung opacity, and viral pneumonia) were included. Our proposed methodology (ViT) achieved remarkable performance, with accuracy rates of 99% for binary classification and 95.25% for multiclass classification.
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