A vision transformer based approach for analysis of plasmodium vivax life cycle for malaria prediction using thin blood smear microscopic images

Neha Sengar, Radim Burget, Malay Kishore Dutta

Abstract: Highlights•Deep learning based efficient classification of plasmodium vivax life cycle to diagnose malaria disease using thin blood smear microscopic images.•Automatic, real-time and robust to real possible inputs and accuracy achieved up to 90.03% on unseen images.•Approach targets complete lifecycle of plasmodium vivax malaria species.•Analysis of different state-of-the-art deep learning models and vision transformer on microscopic dataset.