Automated Mosquito Vector Identification and Malaria Parasite Detection using Deep Learning Approach
Author:
Affiliation:
1. Universiti Malaya,Faculty of Engineering,Department of Biomedical Engineering,Lembah Pantai,Kuala Lumpur
Funder
Universiti Malaya
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10462645/10462729/10462761.pdf?arnumber=10462761
Reference14 articles.
1. An automated malaria cells detection from thin blood smear images using deep learning
2. A Malaria Diagnostic Tool Based on Computer Vision Screening and Visualization of Plasmodium falciparum Candidate Areas in Digitized Blood Smears
3. Counting and Classification of Malarial Parasite From Giemsa-Stained Thin Film Images
4. Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images
5. Effective Performance of Bins Approach for Classification of Malaria Parasite using Machine Learning
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1. Recent Advancements in Detection and Quantification of Malaria Using Artificial Intelligence;UMYU Journal of Microbiology Research (UJMR);2024-09-12
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