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

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
Main Authors: Abdurahman, F. (Author), Aliy, M. (Author), Fante, K.A (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
animal
Animals
blood
Blood
Deep learning
Detection accuracy
Developing countries
diagnostic test
Diagnostic Tests, Routine
Diseases
Feature map
Gold metallography
K-means clustering
Low-resource settings
malaria
Malaria
Malaria diagnosis
Microscopic image
microscopy
Microscopy
Object detection
Object detection algorithms
Object recognition
parasite
Parasites
Plasmodium falciparum
Small object detection
Smart-phone cameras
State of the art
Thick blood smear
YOLOV3
YOLOV4
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