Detection and Classification of Malaria Parasite Based on Improved YOLOv5 Model
Author:
Affiliation:
1. East China Normal University,Shanghai Key Laboratory of Multidimensional Information Processing,Shanghai,China
2. Shanghai University of Electric Power,School of Electronic and Information Engineering,Shanghai,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10373215/10373216/10373282.pdf?arnumber=10373282
Reference19 articles.
1. Malaria − an overview
2. A review of practical techniques for the diagnosis of malaria
3. GGB Color Normalization and Faster-RCNN Techniques for Malaria Parasite Detection
4. Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models
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