Using domain adaptation and transfer learning techniques to enhance performance across multiple datasets in COVID-19 detection

Résumé

This study presents a hybrid neuro-symbolic framework for COVID-19 detection in chest CT that combines multiple deep learning architectures with rule-based reasoning and domain-adversarial adaptation. By aligning features across four heterogeneous public datasets, the system maintains high, site-independent performance (average accuracy = 97.7%, AUC-ROC = 0.996) without retraining. Symbolic rules and Grad-CAM visualizations provide clinician-level interpretability, achieving near-perfect agreement with board-certified radiologists (κ = 0.89). Real-time inference (23.4 FPS) and low cloud latency (1.7 s) meet hospital PACS throughput requirements. Additionally, the framework predicts key treatment outcomes, such as intensive care unit (ICU) admission risk and steroid responsiveness, using retrospective EHR data. Together, these results demonstrate a scalable, explainable solution that addresses cross-institutional generalization and clinical acceptance challenges in AI-driven COVID-19 diagnosis.

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