Chest X-ray imaging remains one of the most widely used diagnostic tools for detecting lung diseases such as pneumonia, tuberculosis, and lung cancer. However, accurate interpretation requires significant expertise and is often time-consuming. This study explores the application of convolutional neural networks (CNNs) for automated detection of lung abnormalities in chest X-ray images. A dataset of labeled chest radiographs was used to train and evaluate multiple deep learning models, including ResNet and VGG architectures. The results demonstrate that CNN-based models achieve high accuracy, sensitivity, and specificity in identifying pathological patterns compared to traditional diagnostic approaches. Despite promising outcomes, challenges such as dataset imbalance, overfitting, and lack of interpretability persist. The study concludes that integrating AI-assisted diagnosis with clinical workflows can enhance efficiency and diagnostic reliability in radiology.

Chest X-ray, Lung disease detection, Convolutional neural networks, Medical imaging, Artificial intelligence

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