Abstract: Medical imaging forms a cornerstone of modern diagnostic healthcare, still manual image interpretation is inten- sive and susceptible to discrepancies among different observers. This work introduces a hybrid deep learning methodology that combines Convolutional Neural Networks (CNNs) with classical image processing techniques to classify chest X-rays into diseased and normal categories. Using transfer learning with ResNet50 and feature fusion involving edge, corner, and texture descriptors, the suggested architectural framework exhibits enhanced efficacy in the detection of pneumonia, tuberculosis, and COVID-19. The dataset used is NIH Chest X-ray14 and supplementary datasets show enhanced accuracy, recall and area under the curve. Furthermore, explainability tools such as Grad-CAM overlays with heat maps provide interpretability and clinical confidence, addressing a major gap in AI-assisted diagnostics.

Index Terms: Deep Learning, Medical Imaging, CNN, Hybrid Model, Chest X-ray, Explainable AI, Transfer Learning, Grad- CAM, Multi-label Classification.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131041