Deep Learning Application for Detection of Malaria

Md Saifur Rahman, Nafiz Rifat, Mostofa Ahsan, Sabrina Islam, Md Chowdhury, Rahul Gomes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Malaria continues to be a significant burden on global health with 247 million clinical episodes and 619,000 deaths. Along with biomedical science, technology, and informatics have begun participating in the quest against malaria. Microscopy techniques are frequently used to detect malaria parasites in infected red blood cells. Giemsa stain has been used to stain blood parasites for over a century. The stain is applied after fixing blood smears in methyl alcohol for 25 to 30 minutes [1]. When stained slides are examined under a microscope, the parasites are easily discernible based on morphology and color. We observed that automating the detection of these slides using deep learning is possible with high accuracy. A comparison between deep learning models reveals ResNets provide better performance.

Original languageEnglish (US)
Title of host publication2023 IEEE International Conference on Electro Information Technology, eIT 2023
PublisherIEEE Computer Society
Pages468-472
Number of pages5
ISBN (Electronic)9781665493765
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Electro Information Technology, eIT 2023 - Romeoville, United States
Duration: May 18 2023May 20 2023

Publication series

Name2023 IEEE International Conference on Electro Information Technology (eIT)

Conference

Conference2023 IEEE International Conference on Electro Information Technology, eIT 2023
Country/TerritoryUnited States
CityRomeoville
Period5/18/235/20/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Deep Learning
  • Malaria
  • ResNet
  • VGG

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