Javaria Amin1
, Muhammad Almas Anjum2
, Abida Sharif3
, Mudassar Raza4
, Seifedine Kadry5, Yunyoung Nam6,*
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 6023-6039, 2022, DOI:10.32604/cmc.2022.019115
- 11 October 2021
Abstract
Malaria is a severe illness triggered by parasites that spreads via mosquito bites. In underdeveloped nations, malaria is one of the top causes of mortality, and it is mainly diagnosed through microscopy. Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups. Therefore, in this study, we present a new idea based on the ensemble quantum-classical framework for malaria classification. The methods comprise three core steps: localization, segmentation, and classification. In the first core step, an improved FRCNN model is proposed for the localization of the infected
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