Budi Sunarko, Djuniadi, Murk Bottema, Nur Iksan, Khakim A. N. Hudaya, Muhammad S. Hanif
Parasite detection is important for the diagnosis of many blood-borne diseases including malaria. As part of a program to develop a fast, accurate, and affordable automatic device for diagnosing malaria, a critical step is to automatically classify individual red blood cells in thin blood smear images. To automatically recognize malaria parasites in an image, this paper presents a red blood cell classification study for malaria diagnosis. To diagnose malaria, the threshold-based segmentation is implemented using the Otsu's method succeeded by the distance transform and statistical classifier. The methods are applied to red blood cell images obtained from Kaggle. These experimental results show that the classification recognizes malaria parasite with 94.60% accuracy, 96.20% specificity, and 93% sensitivity. © Published under licence by IOP Publishing Ltd.
Department of Electrical Engineering, Universitas Negeri Semarang, Indonesia; Flinders University, Australia
