<![CDATA[Tuberculosis (TB) and Pneumonia continue to be among the world’s leading causes of morbidity and mortality, particularly in low- and middle-income countries where access to advanced diagnostic tools remains limited. Conventional radiological interpretation, while effective, heavily depends on the experience and precision of radiologists, resulting in potential subjectivity and diagnostic variability. This study proposes a fully automated classification framework for lung disease detection using a Convolutional Neural Network (CNN) based on the VGG-19 architecture. The model aims to enhance diagnostic accuracy and reliability by leveraging deep learning techniques capable of capturing subtle radiographic patterns that may not be readily identifiable by human observers. A dataset of 3,623 chest X-ray images—divided into Normal, Pneumonia, and Tuberculosis classes—was compiled from Kaggle and Mendeley Data repositories. Preprocessing techniques including Contrast Limited Adaptive Histogram Equalization (CLAHE), cropping, resizing, and normalization were employed to enhance contrast and minimize noise. The model was trained and tested under four data-split configurations (80:20, 70:30, 60:40, and 50:50) to assess generalization capability. The 70:30 configuration achieved optimal performance, recording 96% accuracy, 97% precision, 95% recall, and a 96% F1-score. These findings demonstrate that the VGG-19 model can accurately distinguish between TB, Pneumonia, and Normal cases, providing a reliable foundation for AI-driven medical diagnosis. Future research will focus on dataset expansion, interpretability enhancement using Explainable AI (XAI), and the integration of this model into clinical decision-support systems.]]>
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