用深度学习提升胸片中肺病分类准确率，解决图像质量差和数据不均衡问题。
Deep Learning-Powered Classification of Thoracic Diseases in Chest X-Rays
- 基于预训练模型迁移学习，结合焦点损失缓解类别不平衡。
- InceptionV3模型AUC提升28%，F1分数提高15%。
- 通过Grad-CAM可视化增强可解释性，适合临床辅助诊断场景。
胸部X光在诊断肺炎、结核病和新冠肺炎等呼吸系统疾病中具有关键作用，这些疾病普遍存在且因视觉特征重叠及图像质量差异带来独特诊断挑战。严重类别不平衡与医学图像的复杂性阻碍了自动化分析。本研究采用深度学习技术，包括在预训练模型（AlexNet、ResNet、InceptionNet）上进行迁移学习，通过微调模型并引入焦点损失以应对类别不平衡问题，显著提升了疾病检测与分类性能。Grad-CAM可视化进一步增强了模型可解释性，揭示了影响预测的临床相关区域。例如，InceptionV3模型在AUC上提升了28%，F1分数提高了15%。这些结果表明，深度学习有望改善诊断流程，支持临床决策。
原文摘要 · Abstract (English)
Chest X-rays play a pivotal role in diagnosing respiratory diseases such as pneumonia, tuberculosis, and COVID-19, which are prevalent and present unique diagnostic challenges due to overlapping visual features and variability in image quality. Severe class imbalance and the complexity of medical images hinder automated analysis. This study leverages deep learning techniques, including transfer learning on pre-trained models (AlexNet, ResNet, and InceptionNet), to enhance disease detection and classification. By fine-tuning these models and incorporating focal loss to address class imbalance, significant performance improvements were achieved. Grad-CAM visualizations further enhance model interpretability, providing insights into clinically relevant regions influencing predictions. The InceptionV3 model, for instance, achieved a 28% improvement in AUC and a 15% increase in F1-Score. These findings highlight the potential of deep learning to improve diagnostic workflows and support clinical decision-making.
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