A review synthesizing why deep-learning malaria diagnosis from blood-smear images fails to generalize in practice, and how to fix it: it catalogs data-quality failure modes (class imbalance, limited geographic diversity, annotation variability) and generalization barriers (variation in smear preparation, staining, and imaging equipment), quantifies their impact, and organizes mitigations - GAN-based augmentation, domain adaptation/transfer learning, class-weighted losses, collaborative diverse-dataset development, and explainable AI - into actionable guidance for reliable, equitable diagnostics in resource-limited settings.
A review article synthesizing the data-quality and model-generalization challenges that limit deep-learning malaria detection from blood-smear images, and the strategies that mitigate them. It analyz…