<![CDATA[Malaria diagnostic automation faced critical challenges including severe class imbalance with ratios up to 54:1, limited datasets with 200 to 500 images, and computational inefficiency requiring separate model training for each detection-classification combination. This study developed a multi-model framework with shared classification architecture that trained classification models once on ground truth crops and reused them across all detectors. The framework systematically evaluated three YOLO Medium architectures for parasite detection and six CNN architectures for lifecycle and species classification across four complementary malaria datasets totaling 1,544 microscopy images. Detection achieved 70.84% to 96.27% mAP@50 with high recall of 71.05% to 93.12% minimizing missed parasites. Classification demonstrated dataset-dependent model selection with parameter-efficient EfficientNet models containing 5.3M to 9.2M parameters consistently outperforming ResNet variants with up to 44.5M parameters. EfficientNet-B1 achieved 91.51% accuracy on IML Lifecycle and 98.28% on MP-IDB Species, while EfficientNet-B0 achieved 86.45% on multi-patient MD-2019 dataset. ResNet50 achieved 96.13% on severely imbalanced MP-IDB Stages. Focal Loss optimization with alpha of 1.0 and gamma of 1.5 enabled robust minority class performance with F1-scores between 0.44 and 1.00 on ultra-minority classes demonstrating effective imbalance handling. The compact 46-89 MB models enabled practical deployment on resource-constrained hardware.]]>
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