对比七种模型在胸部X光多病种分类中的表现，发现高效模型也能达到高准确率。
Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Single-Label Chest X-rays
- 统一数据集与训练条件，对比七种主流深度学习模型性能
- ConvNeXt-Tiny准确率达92.31%，MobileNetV2仅350万参数却达90.42%准确率
- 轻量模型适合资源受限场景，且对新冠、结核等病种识别近乎完美
胸部X光仍是全球肺部与心脏疾病的主要诊断工具，但其准确性受放射科医生短缺和观察者差异影响。本研究系统比较了七种深度学习架构在多类胸病分类中的表现：ConvNeXt-Tiny、DenseNet121、DenseNet201、ResNet50、ViT-B/16、EfficientNetV2-M 和 MobileNetV2。基于三个公开数据集构建了包含18,080张胸片的平衡数据集，涵盖心大、新冠、正常、肺炎和结核五类疾病，并按患者级别划分以防止数据泄露。所有模型均使用ImageNet预训练权重、标准化预处理和一致超参数训练。全部模型测试准确率超过90%。ConvNeXt-Tiny表现最佳（准确率92.31%，AUROC 95.70%），MobileNetV2参数最省（350万参数，准确率90.42%，AUROC 94.10%），训练仅需48分钟。新冠与结核分类几乎完美（各模型AUROC ≥ 99.97%），而正常、心大与肺炎因影像特征重叠更具挑战性。Grad-CAM可视化显示各病种注意力模式具临床一致性。结果表明，无需大量计算资源即可实现高精度多病种分类，对资源丰富与匮乏医疗环境均有重要应用价值。
原文摘要 · Abstract (English)
Chest X-ray imaging remains the primary diagnostic tool for pulmonary and cardiac disorders worldwide, yet its accuracy is hampered by radiologist shortages and inter-observer variability. This study presents a systematic comparative evaluation of seven deep learning architectures for multi-class chest disease classification: ConvNeXt-Tiny, DenseNet121, DenseNet201, ResNet50, ViT-B/16, EfficientNetV2-M, and MobileNetV2. A balanced dataset of 18,080 chest X-ray images spanning five disease categories (Cardiomegaly, COVID-19, Normal, Pneumonia, and Tuberculosis) was constructed from three public repositories and partitioned at the patient level to prevent data leakage. All models were trained under identical conditions using ImageNet-pretrained weights, standardized preprocessing, and consistent hyperparameters. All seven architectures exceeded 90% test accuracy. ConvNeXt-Tiny achieved the highest performance (92.31% accuracy, 95.70% AUROC), while MobileNetV2 emerged as the most parameter-efficient model (3.5M parameters, 90.42% accuracy, 94.10% AUROC), completing training in 48 minutes. Tuberculosis and COVID-19 classification was near-perfect (AUROC >= 99.97%) across all architectures, while Normal, Cardiomegaly, and Pneumonia presented greater challenges due to overlapping radiographic features. Grad-CAM visualizations confirmed clinically consistent attention patterns across disease categories. These findings demonstrate that high-accuracy multi-disease chest X-ray classification is achievable without excessive computational resources, with important implications for AI-assisted diagnosis in both resource-rich and resource-constrained healthcare settings.
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