A review-style preprint asserts that class imbalance lowers malaria-detection F1 by 20% and that GAN-based augmentation and transfer learning raise accuracy and sensitivity, but it provides no experimental evidence.
Malaria rapid diagnostic t est performance : re- sults of WHO product testing of malaria RDTs : round 5 (2013)
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Addressing Challenges in Data Quality and Model Generalization for Malaria Detection
A review-style preprint asserts that class imbalance lowers malaria-detection F1 by 20% and that GAN-based augmentation and transfer learning raise accuracy and sensitivity, but it provides no experimental evidence.