Accurate diagnosis of pulmonary lesions, pneumonia and COVID-19 in the early stages is difficult due to overlapping symptoms and major logistic limitations of advanced molecular testing techniques. In this study, a computational approach is proposed in which the DenseNet121 architecture and the Contrast Limited Adaptive Histogram Equalization (CLAHE) are combined for analysis of full frame thoracic digital radiograph. The system reduces selection bias and computational latency while evaluating uncropped thoracic fields, preserving peripheral lung textures and minimizing interference from surrounding structures such as the rib cage and heart. Evaluation against an independent blind dataset of 50 images yielded a classification accuracy of 92%. The framework showed robust screening ability for infectious diseases with a recall of 1.00 for both COVID-19 and Viral Pneumonia, with F1-scores of 0.92 and 0.97, respectively. Interpretability validation with Gradient-weighted Class Activation Mapping (Grad-CAM) confirmed the network’s focus on basal and peripheral pulmonary features, although visual inspection indicated a potential for shortcut learning due to radiopaque non-anatomical markers. This architecture is not intended to replace standard clinical diagnostic procedures; instead, it serves as a scalable triage layer to optimize clinical resource allocation, reduce specialist fatigue, and provide a screening tool medical centers and resource-limited clinics.