The global spread of lung diseases like COVID-19 and pneumonia has challenged medical specialists in accurate di- agnosis and management. Chest X-ray (CXR) images are essential diagnostic tools, but subtle differences in disease imaging characteristics make identification difficult. Traditional methods depend on radiologists’ expertise, leading to interpretation vari- ations and potential delays. To enhance accuracy and efficiency, this study introduces "LungNet," a lightweight deep learning architecture for classifying epidemic lung diseases. The model was trained and evaluated on a diverse CXR dataset, including images of COVID-19, pneumonia, and normal lungs. Preprocessing techniques and data augmentation were applied to optimize performance and address data variability and class imbalance. LungNet achieved a training accuracy of 100% and a testing ac- curacy of 96%. Additionally, Gradient-weighted Class Activation Mapping (GRAD-CAM) was used to improve interpretability. This research advances healthcare outcomes by addressing challenges in disease identification from CXR images.
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