MACHINE LEARNING BASED DETECTION OF MALARIA INFECTION THROUGH BLOOD SAMPLE ANALYSIS FOR MALARIA DIAGNOSIS
DOI:
https://doi.org/10.48047/Keywords:
Automated Malaria Detection, Plasmodium Parasites, Deep Learning, Blood Smear Analysis, Image Processing, Portable Diagnostic Devices.Abstract
Plasmodium parasites, which are responsible for the life-threatening disease known as malaria, are transmitted through infected mosquitoes on a global scale. Malaria continues to be a significant public health concern in many locations across the world. The diagnosis of malaria infection in its early stages and with high accuracy is essential for the timely treatment and management of the disease. The automated malaria detection method may be included into portable diagnostic instruments, which enables medical personnel to conduct malaria tests that are both quick and accurate even in settings that are resource-constrained or located in distant areas.
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References
. WHO. World Malaria Report 2022. Available online: https://www.who.int/teams/global malaria-programme/reports/world-malaria-report-2022 (accessed on 1 March 2023).
. WHO. World Malaria Report 2021: An In-Depth Update on Global and Regional Malaria Data and Trends. Available online: https://www.who.int/teams/global-malaria programme/reports/world-malaria-report-2021 (accessed on 1 September 2022).
. Yang, F.; Poostchi, M.; Yu, H.; Zhou, Z.; Silamut, K.; Yu, J.; Maude, R.J.; Jaeger, S.; Antani, S. Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J. Biomed. Health Inform. 2019, 24, 1427–1438.
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