Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

Abstract: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.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2607.26580 [cs.CV]

(or arXiv:2607.26580v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.26580 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

International Journal of Drug Delivery Technology (IJDDT), Vol. 16, Issue 26s, pp.1101-1112, 2026

https://doi.org/10.25258/ijddt.16.26s.117 Focus to learn more DOI(s) linking to related resources

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