Abstract: Malaria is a mosquito-borne infectious disease of humans and other animals caused by parasites (a type of microorganism) of the genus Plasmodium. Infection is initiated by a bite from an infected female mosquito, which introduces the parasites via its saliva into the circulatory system, and ultimately to the liver where they mature and reproduce. The disease causes symptoms that typically include fever and headache, which in severe cases can progress to coma or death. The diagnosis of malaria is microscopy in which the blood slide is examined under a microscope, but the reliability, accuracy and timely diagnosis of the results are highly based on the proficiency of the technician examining the slide. False Detection can occur in the case of poorly skilled technician. In this research work we have proposed a system for automating the manual work done by a technician in order to cut down the human error and increasing the accuracy of the malaria diagnosis. This approach will be beneficial for the rural areas, with a scarcity of experts.

Keywords: Image Processing, RGB color, KNN, Naïve Bayes, HSV segmentation, RDT, Otsu method.

Downloads: | DOI: 10.17148/IJIREEICE.2019.7205