Explainable Deep Learning for COVID-19 and Chest Disease Detection: A Dual-Model Approach Using DenseNet121 and UNet

The correct interpretation of chest radiographs (CXR) still remains a challenge in the clinical practice, particularly in a mass emergency such as the COVID-19 pandemic, where a fast and accurate approach to diagnosis is of utmost importance. In the proposed work, an on-segmentation pipeline based on integrated classification combines four deep CNN models. (DenseNet121, ResNet50, EfficientNetB0, and ConvNeXt-Tiny) on the classification of various. Images on CXR to four categories COVID-19, lung opacities, viral pneumonia and normal. We introduce a novel hybrid architecture that integrates DenseNet121’s dense connectivity with U-Net’s encoder–decoder framework, enhanced with channel-wise attention mechanisms for improved spatial feature learning. Developed a balanced data set of each class had 5380 images and 1345 samples to represent fair model learning. All models were trained using a steady stream of data–data augmentation using Albumentations, early stopping and a maximum of 50 epochs. Our comprehensive evaluation includes accuracy, precision, recall, F1-score, IoU, Dice coefficient, statistical significance testing (Wilcoxon signed—rank test, p