Leveraging Deep Learning Techniques for Definitude Malaria Detection Using Microscopic Images
Rakesh Kumar · International Journal for Research in Applied Science and Engineering Technology · 2025
Abstarct: Artificial intelligence (AI) is transforming malaria detection by enabling faster, more accurate diagnosis through the analysis of blood samples with high precision, identifying malaria parasites within minutes. Malaria, a potentially fatal disease caused by the Plasmodium bacterium and transmitted via mosquito bites,requiresearlyandaccuratediagnosisforeffective treatment and prevention. Current systems use the ResNetalgorithmtoanalyzeimages,identifyingpatterns inmalariaparasitesto differentiate betweeninfectedand healthy cells. However, these systems suffer from low prediction accuracy, limiting their realworld effectiveness. To address this, we propose the use of EfficientNet and Vision Transformers (ViT), which applyNLPtechniquesdirectlytoimagepatches,treating each patch as a token to capture long-range dependencies and global context, thereby enhancing efficiency, accuracy, and precision.