用VGG16、VGG19和ResNet50分析肺部X光片，识别肺炎等疾病。
Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models
- 使用VGG16、VGG19和ResNet50对肺部X光图像分类。
- ResNet50准确率最高，优于其他两个模型。
- 适合医疗AI研究者和临床辅助诊断系统开发者。
随着呼吸系统疾病病例增加，亟需早期检测与精准诊断。卷积神经网络在影像诊断中表现优异。本研究探讨了VGG16、VGG19和ResNet50在基于X光图像分类肺部疾病（包括肺炎、结核、肺癌和正常肺）中的潜力。这些深度学习模型在大量X光图像上训练后进行性能评估。结果表明，尽管三者均表现良好，但ResNet50因高效与高准确率优于其他模型。我们认为这些深度学习模型未来可成功应用于肺病诊断实践，有助于早期发现疾病并改善患者预后。
原文摘要 · Abstract (English)
With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately. Convolutional neural networks have given promising results when used for diagnosing diseases using imaging tests. In this study, we investigate the potential of applying deep learning algorithms such as VGG16, VGG19, and ResNet50 for classification of lung ailments based on X-ray images. A detailed analysis of the aforementioned models' performances was conducted to assess how well they can classify various types of lung ailments, including pneumonia, tuberculosis, lung cancer, and normal lungs. In order to do that, these deep learning models were trained on a vast amount of X-ray images. The results of our study show that while all three models provide good results, ResNet-50 performs best in comparison with other models due to its efficiency and high level of accuracy. We believe that these deep learning models can be successfully implemented in the practice of diagnosing pulmonary diseases in the future. It helps with early disease detection and improves patient outcomes.
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