A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images-Reference-Cited by-同舟云学术

A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images

Published:2021-10-29 Issue: Volume: Page:
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Yang Ziheng^ORCID,Benhabiles Halim,Hammoudi Karim,Windal Feryal,He Ruiwen,Collard Dominique

Funder

ISEN Junia

h4dc project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-021-06604-4.pdf

Reference59 articles.

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3. Abbas SS, Dijkstra TM (2020) Detection and stage classification of plasmodium falciparum from images of giemsa stained thin blood films using random forest classifiers. Diagn Pathol 15(1):1–11

4. Bailey JW, Williams J, Bain BJ, Parker-Williams J, Chiodini PL, General Haematology Task Force of the British Committee for Standards in Haematology (2013) of the British Committee for Standards in Haematology, Guideline: the laboratory diagnosis of malaria. Br J Haematol 163(5):573–580

5. Caraballo H, King K (2014) Emergency department management of mosquito-borne illness: malaria, dengue, and west nile virus. Emerg Med Pract 16(5):1–23

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