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This study evaluates the effectiveness of deep learning architectures VGG16, VGG19, and ResNet-50 for classifying lung diseases from X-ray images, addressing the urgent need for early and accurate diagnosis of pulmonary disorders. Using a large dataset, the research reveals that ResNet-50 outperforms the other models in both accuracy and efficiency when distinguishing between conditions such as pneumonia, tuberculosis, and lung cancer. The findings underscore the potential of these CNNs to assist healthcare professionals in improving diagnostic processes and patient outcomes.
ResNet-50 achieves superior accuracy in classifying lung diseases from X-ray images, setting a new benchmark for automated diagnostic tools in radiology.
This research investigates the application of deep learning models, specifically VGG16, VGG19, and ResNet-50, for the classification of lung diseases from X-ray images. With the increasing prevalence of pulmonary disorders, early and accurate diagnosis is crucial for effective medical intervention. Convolutional Neural Networks (CNNs) have shown promise in automating this process. In this study, a comprehensive evaluation of VGG16, VGG19, and ResNet-50 architectures is conducted to determine their effectiveness in distinguishing between various lung pathologies, including pneumonia, tuberculosis, lung cancer, and normal lung conditions. A large dataset of X-ray images is used for training and testing these models. The results demonstrate the superior performance of ResNet-50 in terms of accuracy and efficiency, followed by VGG19 and VGG16. The models exhibit potential for clinical use in assisting radiologists and healthcare professionals in diagnosing lung diseases more swiftly and accurately. This research contributes to the ongoing efforts in the integration of deep learning techniques into the medical field, facilitating early disease detection and better patient outcomes.