Malaria Detection Using Supervised Learning

Abstract

Malaria is a deadly, infectious and life-threatening mosquito-borne blood disease caused by Plasmodium parasites. The conventional and most standard way of diagnosing malaria is by visually examining blood smears via microscope for parasite-infected red blood cells under the microscope by qualified technicians. This method is inefficient and time consuming and the diagnosis depends on the experience and the knowledge of the person doing the examination. Automated image recognition technology based on image processing has previously been applied to malaria blood smears for diagnosis. However, practical performance has so far not been limited. It gives us all the impetus to make the diagnosis and diagnosis of malaria faster, easier and more efficient. Our main goal is to create a model that can detect cells from multiple cell images of a thin blood smear on a standard microscope slide and classify them as infected or not by early or effective testing using image processing. And also classify infected cell images using machine learning. Key Words: Malaria, Falciparum, Watershed, Morphological Segmentation, Edge Detection, and Segmentation.