Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models.

ABSTRACT:

Background

Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the "gold standard" for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists' diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides.

Results

YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasite

SUBMITTER: Abdurahman F 

PROVIDER: S-EPMC7938584 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature