Towards Transparent Diagnostics: Investigating Architectural Trade-offs and Explainability in Malaria Detection
More than 80 countries have reported malaria cases with 610 thousand deaths and are projected to increase. Identifying malaria early and accurately helps save lives and effective way to diagnose malaria is through microscopic methods that are labor intensive and require experts with special equipment. Deep learning (DL) has shown promising results in medical diagnosis. Here, we explored various DL models: ResNet18, MobileNetV2, EfficientNet-B2, VGG19 and proposed model ResNet18+TTA (ResNet18 backbone with modified classification head and test time augmentation) for detecting malaria presence using blood smears taken from the NIH Malaria dataset. Our experiment shows MobileNetV2 achieved 9...
| Authors | Suman Kunwar, Avishek Dangol |
|---|---|
| Primary category | cs.CV |
| Published | 16 September 2026 |
| Last updated at source | 5 October 2026 |
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