The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Ali Mansourian

Ali Mansourian

Professor

Ali Mansourian

A web-based intelligence platform for diagnosis of malaria in thick blood smear images : A case for a developing country

Author

  • Rose Nakasi
  • Jeremy Francis Tusubira
  • Aminah Zawedde
  • Ali Mansourian
  • Ernest Mwebaze

Summary, in English

Malaria is a public health problem which affects developing countries world-wide. Inadequate skilled lab technicians in remote areas of developing countries result in untimely diagnosis of malaria parasites making it hard for effective control of the disease in highly endemic areas. The development of remote systems that can provide fast, accurate and timely diagnosis is thus a necessary innovation. With availability of internet, mobile phones and computers, rapid dissemination and timely reporting of medical image analytics is possible. This study aimed at developing and implementing an automated web-based Malaria diagnostic system for thick blood smear images under light microscopy to identify parasites. We implement an image processing algorithm based on a pre-trained model of Faster Convolutional Neural Network (Faster R-CNN) and then integrate it with web-based technology to allow easy and convenient online identification of parasites by medical practitioners. Experiments carried out on the online system with test images showed that the system could identify pathogens with a mean average precision of 0.9306. The system holds the potential to improve the efficiency and accuracy in malaria diagnosis, especially in remote areas of developing countries that lack adequate skilled labor.

Department/s

  • MECW: The Middle East in the Contemporary World
  • Dept of Physical Geography and Ecosystem Science

Publishing year

2020-06-01

Language

English

Pages

4238-4244

Publication/Series

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volume

2020-June

Document type

Conference paper

Publisher

IEEE Computer Society

Topic

  • Medical and Health Sciences
  • Computer and Information Science
  • Earth and Related Environmental Sciences

Keywords

  • Machine Learning (ML)
  • Artificial Intelligence (AI)

Conference name

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020

Conference date

2020-06-14 - 2020-06-19

Conference place

Virtual, Online, United States

Status

Published

ISBN/ISSN/Other

  • ISSN: 2160-7508
  • ISSN: 2160-7516
  • ISBN: 9781728193601