Resolution-Aware Malaria Screening: Do Super-Resolved RBC Images Improve CNNs and Vision Transformers? - Oliveira, Igor; Negrão, Arthur; G. Júnior, Ederson N. F.; Silva, Guilherme; Vieira, Matheus; Silva, Pedro (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21321, 2026)
Automated malaria screening from microscopy images can improve diagnostic scalability in resource-limited settings, but detecting infected red blood cells (RBCs) is challenging due to subtle morphological cues and resolution constraints. We study resolution-aware malaria classification on the NIH Malaria Dataset using EfficientNet-B0 and ViTs, evaluating transfer learning, optimization strategies, and ESRGAN-based super-resolution preprocessing. Results show that cosine decay and transfer learning are critical for robust performance, enabling EfficientNet-B0 to reach 96% accuracy and recall, while ESRGAN with ViT achieves 97% accuracy and 98% recall, matching SOTA results while reducing false negatives.