AI 中文总结
本研究将VGG16、VGG19、ResNet50用于肺部X光图像疾病分类，训练大量X光图像后发现ResNet50性能最优，可助力肺部疾病早期诊断。
AI 中文摘要
随着呼吸道疾病病例数量的增加，迫切需要对其进行早期检测和准确诊断。卷积神经网络在利用影像检查诊断疾病方面已取得良好效果。本研究探讨将VGG16、VGG19和ResNet50等深度学习算法应用于基于X光图像的肺部疾病分类的潜力。为评估上述模型对各类肺部疾病（包括肺炎、肺结核、肺癌及正常肺部）的分类性能，对其表现展开详细分析，将这些深度学习模型在大量X光图像上进行训练。研究结果显示，尽管这三个模型均表现良好，但ResNet-50凭借其高效性和高准确率，相比其他模型表现最佳。我们认为这些深度学习模型未来可成功应用于肺部疾病诊断实践，助力疾病早期检测并改善患者预后。
英文摘要
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.
Comments15 pages, 10 figures, 3 tables, International Journal of Drug Delivery Technology (IJDDT)
Journal refInternational Journal of Drug Delivery Technology (IJDDT), Vol. 16, Issue 26s, pp.1101-1112, 2026
