Full description
Accurate and rapid diagnosis of malaria parasites before treatment is of utmost importance to reduce malaria mortality and morbidity. While microscopy remains the gold standard and rapid detection test (RDTs) is the present mainstay of malaria diagnosis in most large health clinics and hospitals, the quality of microscopy is frequently inadequate, and the accuracy of RDTs is reportedly falling due to specific parasite antigenic genes mutations. The detection is cumbersome in specifically remote and rural areas, which can impede the diagnosis and treatment. Delay in receiving treatment for uncomplicated malaria is often reported to increase the risk of developing severe malaria, but access to treatment remains low in most rural areas, where the burden of disease is high. The objective of this project is to develop an innovative cyber-critical technology framework for early malaria pathogen detection. The proposed translational technology solution can be useful for other diseases and regions globally.Data time period: 2024-01-01 to 2025-03-20
Subjects
Deep Learning |
Malaria Pathogen |
Medical Imaging |
Microscope Images |
Object Detection |
Other information and computing sciences not elsewhere classified |
thick blood smear images |
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Identifiers
- DOI : 10.25946/25357483.V1
