DAViT: A Domain-Adapted Vision Transformer for Automated Pneumonia Detection and Explanation Using Chest X-Ray Images
Pneumonia, a leading cause of mortality worldwide, especially among children under five, is typically diagnosed via chest X-rays. However, detecting it is challenging as expert radiologists must discern subtle patterns. Artificial intelligence (AI) offers a scalable alternative by automating diagnos...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11031440/ |
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| Summary: | Pneumonia, a leading cause of mortality worldwide, especially among children under five, is typically diagnosed via chest X-rays. However, detecting it is challenging as expert radiologists must discern subtle patterns. Artificial intelligence (AI) offers a scalable alternative by automating diagnosis through deep learning (DL) models. Despite progress, current methods face two key limitations: 1) reliance on CNNs that capture local but may overlook global features, and 2) the use of pre-trained models from natural image datasets like ImageNet, which lack the contextual relevance of medical imaging, leading to suboptimal performance. To address these challenges, we propose DAViT (Domain-Adapted Vision Transformer), a hybrid architecture that combines Vision Transformers (ViTs) and shallow CNNs with domain adaptation. The ViT leverages self-attention to capture global features, while the CNN extracts local ones. To mitigate domain differences, we adapt the model using a diverse chest X-ray dataset. We evaluate DAViT on a real-world dataset of 5,856 chest X-rays. The results demonstrate that DAViT achieves state-of-the-art performance with a 97% F1-score and 96% AUC for pneumonia detection, outperforming twelve baseline methods. For pneumonia type classification, DAViT achieves an 81% F1-score and 84% AUC, outperforming baselines by 25% to 74%. An ablation study highlights the critical contributions of domain adaptation, ViT, and CNN components, collectively enhancing performance by 21%. Finally, we apply Grad-CAM on top of DAViT to generate interpretable heatmaps that highlight relevant areas for bacterial and viral pneumonia cases, providing insights to assist medical practitioners in decision-making. These findings indicate the potential of DAViT to assist clinicians in pneumonia diagnosis through improved model accuracy and interpretability. The training code and pre-trained models are available at <uri>https://github.com/awsm-research/DAViT</uri> |
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| ISSN: | 2169-3536 |