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

T. Grace Shalini (), S. S. Krishikaa Mathi Bharathi () and T. Padmapriya ()
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T. Grace Shalini: SRM Institute of Science and Technology, Department of Computational Intelligence
S. S. Krishikaa Mathi Bharathi: SRM Institute of Science and Technology, Department of Computational Intelligence
T. Padmapriya: SRM Institute of Science and Technology, Department of Computational Intelligence

A chapter in AI in Smart and Secure Healthcare, 2026, pp 3-43 from Springer

Abstract: Abstract 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

Keywords: Chest X-ray (CXR); COVID-19 detection; Deep learning; Convolutional neural networks (CNN); DenseNet121; UNet encoder; Medical image classification; Semantic segmentation; Grad-CAM; Explainable AI (XAI); Radiograph interpretation; Clinical decision support; Image augmentation; Model generalization; Thoracic imaging (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-15092-9_1

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