Ali Mansourian
Professor
A web-based intelligence platform for diagnosis of malaria in thick blood smear images : A case for a developing country
Författare
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.
Avdelning/ar
- MECW: The Middle East in the Contemporary World
- Institutionen för naturgeografi och ekosystemvetenskap
Publiceringsår
2020-06-01
Språk
Engelska
Sidor
4238-4244
Publikation/Tidskrift/Serie
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volym
2020-June
Dokumenttyp
Konferensbidrag
Förlag
IEEE Computer Society
Ämne
- Medical and Health Sciences
- Computer and Information Science
- Earth and Related Environmental Sciences
Nyckelord
- 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/Övrigt
- ISSN: 2160-7508
- ISSN: 2160-7516
- ISBN: 9781728193601