Machine and deep learning methods in identifying malaria through microscopic blood smear: A systematic review-Reference-Cited by-同舟云学术

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Machine and deep learning methods in identifying malaria through microscopic blood smear: A systematic review

Published:2024-07 Issue: Volume:133 Page:108529
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Sukumarran Dhevisha,Hasikin Khairunnisa^ORCID,Mohd Khairuddin Anis Salwa,Ngui Romano,Wan Sulaiman Wan Yusoff,Vythilingam Indra^ORCID,Divis Paul C.S.^ORCID

Publisher

Elsevier BV

Reference66 articles.

1. Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models;Abdurahman;BMC Bioinf.,2021

2. DeepFMD: computational analysis for malaria detection in blood-smear images using deep-learning features;Abubakar;Appl. Syst. Innovat.,2021

3. Real-life evaluation of deep learning models trained on two datasets for Plasmodium falciparum detection with thin blood smear images at 500x magnification;Acherar;Inform. Med. Unlocked,2022

4. The impact of artificial intelligence in medicine on the future role of the physician;Ahuja;PeerJ,2019

5. Intelligent deep transfer learning based malaria parasite detection and classification model using biomedical image;Alassaf;Comput. Mater. Continua (CMC),2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review;Electronics;2024-08-11

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