DLRFNet: deep learning with random forest network for classification and detection of malaria parasite in blood smear-Reference-Cited by-同舟云学术

DLRFNet: deep learning with random forest network for classification and detection of malaria parasite in blood smear

Published:2024-01-10 Issue:23 Volume:83 Page:63593-63615
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Murmu Anita^ORCID,Kumar Piyush

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-17866-6.pdf

Reference55 articles.

1. World Health Organization (2021) World malaria report. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2021. Accessed 25 Sep 2022

2. Kettelhut MM, Chiodini PL, Edwards H, Moody A (2003) External quality assessment schemes raise standards: evidence from the UKNEQAS parasitology subschemes. J Clin Pathol 56(12):927–932. https://doi.org/10.1136/jcp.56.12.927

3. Delahunt C B, Mehanian C, Hu L, McGuire S K, Champlin C R, Horning MP, Thompon CM (2015) Automated microscopy and machine learning for expert-level malaria field diagnosis. In: 2015 IEEE global humanitarian technology conference (GHTC), 393-399. https://doi.org/10.1109/GHTC.2015.7344002

4. Muralidharan V, Dong Y, Pan W D (2016) A comparison of feature selection methods for machine learning based automatic malarial cell recognition in wholeslide images. In 2016 IEEE-EMBS international conference on biomedical and health informatics (BHI), pp 216–219. https://doi.org/10.1109/BHI.2016.7455873

5. Song L, Liu X, Chen S, Liu S, Liu X, Muhammad K, Bhattacharyya S (2022) A deep fuzzy model for diagnosis of COVID-19 from CT images. Appl Soft Comput 122:108883. https://doi.org/10.1016/j.asoc.2022.108883

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