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

Fetulhak AbdurahmanKinde Anlay FanteMohammed Aliy

Published in: BMC bioinformatics (2021)

The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.

Keyphrases

plasmodium falciparum
convolutional neural network
deep learning
optical coherence tomography
loop mediated isothermal amplification
mass spectrometry
machine learning
trypanosoma cruzi