融合CNN、数据增强与ViT的肺病检测方法，准确率达98%。
Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers
- 结合ResNet50与Vision Transformer，利用数据增强提升模型泛化能力。
- 在多类肺病数据集上达到98%准确率和99%的AUC值。
- 适合医学影像分析、AI辅助诊断等场景应用。
肺部疾病是重大公共卫生问题，亟需精准高效的诊断技术。本文提出一种基于卷积神经网络（CNN）、数据增强、ResNet50与视觉变换器（ViT）的方法，用于从医学影像中检测肺部病变。实验使用包含肺癌、肺炎、结核和纤维化等疾病的X光片与CT扫描数据集。通过准确率、敏感性、特异性及ROC曲线下面积（AUC）等指标，将该方法与其他现有方法进行对比。结果表明，该方法在各项指标上均优于其他方法，准确率达到98%，AUC为99%。结论显示，该方法是一种有效且具有前景的肺部疾病医学影像诊断工具。
原文摘要 · Abstract (English)
Pulmonary diseases are a public health problem that requires accurate and fast diagnostic techniques. In this paper, a method based on convolutional neural networks (CNN), Data Augmentation, ResNet50 and Vision Transformers (ViT) is proposed to detect lung pathologies from medical images. A dataset of X-ray images and CT scans of patients with different lung diseases, such as cancer, pneumonia, tuberculosis and fibrosis, is used. The results obtained by the proposed method are compared with those of other existing methods, using performance metrics such as accuracy, sensitivity, specificity and area under the ROC curve. The results show that the proposed method outperforms the other methods in all metrics, achieving an accuracy of 98% and an area under the ROC curve of 99%. It is concluded that the proposed method is an effective and promising tool for the diagnosis of pulmonary pathologies by medical imaging.
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