A web-based intelligence platform for diagnosis of malaria in thick blood smear images : A case for a developing country
(2020) 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2020-June. p.4238-4244- Abstract
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... (More)
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.
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- author
- Nakasi, Rose ; Tusubira, Jeremy Francis ; Zawedde, Aminah ; Mansourian, Ali LU and Mwebaze, Ernest
- organization
- publishing date
- 2020-06-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Machine Learning (ML), Artificial Intelligence (AI)
- host publication
- Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
- series title
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
- volume
- 2020-June
- article number
- 9150682
- pages
- 7 pages
- publisher
- IEEE Computer Society
- conference name
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
- conference location
- Virtual, Online, United States
- conference dates
- 2020-06-14 - 2020-06-19
- external identifiers
-
- scopus:85090160037
- ISSN
- 2160-7508
- 2160-7516
- ISBN
- 9781728193601
- DOI
- 10.1109/CVPRW50498.2020.00500
- language
- English
- LU publication?
- yes
- id
- e9db13db-cab3-40d1-9d63-94f03b9d33b1
- date added to LUP
- 2020-09-16 09:02:38
- date last changed
- 2024-04-03 13:09:38
@inproceedings{e9db13db-cab3-40d1-9d63-94f03b9d33b1,
abstract = {{<p>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.</p>}},
author = {{Nakasi, Rose and Tusubira, Jeremy Francis and Zawedde, Aminah and Mansourian, Ali and Mwebaze, Ernest}},
booktitle = {{Proceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020}},
isbn = {{9781728193601}},
issn = {{2160-7508}},
keywords = {{Machine Learning (ML); Artificial Intelligence (AI)}},
language = {{eng}},
month = {{06}},
pages = {{4238--4244}},
publisher = {{IEEE Computer Society}},
series = {{IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}},
title = {{A web-based intelligence platform for diagnosis of malaria in thick blood smear images : A case for a developing country}},
url = {{http://dx.doi.org/10.1109/CVPRW50498.2020.00500}},
doi = {{10.1109/CVPRW50498.2020.00500}},
volume = {{2020-June}},
year = {{2020}},
}