Malaria is a lethal disease caused by a parasite known as Plasmodium. The current common technique of med- ical diagnosis for the disease includes manually identifying and counting the parasitized cells by performing light microscopy of thick and thin stained blood smears. Since this procedure is car- ried out manually, there is always a scope of misdiagnosis either due to poor familiarity with the procedure by a diagnostician or problems in accessing and acquiring the proper equipments required for diagnosis. In this project, we explore different techniques for the automation and unsupervised detection of malarial parasites.
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