Lightweight deep learning models perform comparably to larger ones for malaria detection, but explainability techniques degrade under image corruption even when predictions remain accurate.
Efficient deep learning for medical imaging: Bridging the gap between high-performance ai and clinical deployment.arXiv preprint arXiv:2602.00910,
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Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis
Lightweight deep learning models perform comparably to larger ones for malaria detection, but explainability techniques degrade under image corruption even when predictions remain accurate.