Authors:
Alan Alves
1
and
Bruno Motta de Carvalho
2
Affiliations:
1
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte - Campus Nova Cruz, Av. José Rodrigues de Aquino Filho, Nova Cruz, Brazil
;
2
Departamento de Informática e Matemática Aplicada, Universidade Federal do Rio Grande do Norte, Rua Cel. João Medeiros, Natal, Brazil
Keyword(s):
Malaria, Computer Vision, RBC Counting.
Abstract:
Malaria is a disease caused by a parasite that is transmitted to humans through the bites of infected mosquitoes. There were an estimated 247 million cases of malaria in 2021, with an estimated number of 619,000 deaths. One of the tasks in diagnosing malaria and prescribing the correct course of treatment is the computation of parasitemia, that indicates the level of infection. The parasitemia can be computed by counting the number of infected Red Blood Cells (RBCs) per µL or the percentage of infected RBCs in 500 to 2,000 RBCs, depending on the used protocol. This work aims to test several techniques for segmenting and counting red blood cells on thin blood films. Popular methods such as Otsu, Watershed, Hough Transform, combinations of Otsu with Hough Transform and convolutional neural networks such as U-Net, Mask R-CNN and YOLO v8 were used. The results obtained were compared with two other published works, the Malaria App and Cell Pose. As a result, one of our implemented methods
obtained a higher F1 score than previous works, especially in the scenario where there are clumps or overlapping cells. The methods with the best results were YOLO v8 and Mask R-CNN.
(More)