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

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...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 22; no. 1; pp. 112 - 17
Main Authors:	Abdurahman, Fetulhak, Fante, Kinde Anlay, Aliy, Mohammed
Format:	Journal Article
Language:	English
Published:	London BioMed Central 08.03.2021 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Animals Bioinformatics Biomedical and Life Sciences Blood Cameras Classification Cluster analysis Clustering Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer-aided medical diagnosis Datasets Deep learning Developing countries Diagnosis Diagnostic Tests, Routine Image processing LDCs Life Sciences Low income groups Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Malaria Malaria - blood Malaria - diagnosis Medical examination Medical imaging Methods Microarrays Microscope and microscopy Microscopy Model accuracy Object detection Object recognition Parasites Performance evaluation Plasmodium falciparum Research Article Smartphones Support vector machines Thick blood smear Vector quantization Vector-borne diseases YOLOV3 Ethiopia Africa Thick blood smear Deep learning Malaria Object detection YOLOV3 YOLOV4 Feature map Plasmodium falciparum
ISSN:	1471-2105, 1471-2105
Online Access:	Get full text
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