Optimized Lung Disease Detection Using Transfer Learning with VGG-16 for Chest X-Ray Analysis

Lung diseases like Pneumonia and Tuberculosis continue to pose significant public health challenges, claiming numerous lives worldwide. Early detection is crucial for effective intervention and treatment. Chest X-rays are a widely used diagnostic tool for identifying these conditions. In this study, we propose a deep learning-based approach leveraging a Convolutional Neural Network (CNN) architecture to diagnose Pneumonia and Tuberculosis from chest X-ray images. Specifically, we employ the VGG-16 model, a transfer learning framework pre-trained on large-scale datasets, to classify lung diseases. The model is trained, tested, and validated on a preprocessed chest X-ray dataset, with iterative optimization to minimize errors and loss. Compared to conventional diagnostic methods like blood tests and sputum analysis, our deep learning approach significantly reduces diagnosis time while maintaining accuracy. As more annotated datasets become available over time, the performance and accuracy of deep learning models will continue to improve, enhancing their utility in clinical applications.