Malaria parasite detection and classification using CNN and YOLOv5 architectures

• Main Authors: Wan Mohd Razin, Wan Rasyidah, Gunawan, Teddy Surya, Kartiwi, Mira, Md. Yusoff, Nelidya
• Format: Conference or Workshop Item
• Language: English; English; English
• Published: IEEE 2022
• Subjects: TK7885 Computer engineering
• Online Access: http://irep.iium.edu.my/101860/7/101860_Malaria%20parasite%20detection%20and%20classification%20using%20CNN%20n%20YOLOv5%20arc.pdf; http://irep.iium.edu.my/101860/13/101816_Malaria%20parasite%20detection%20and%20classification%20using%20CNN%20and%20YOLOv5%20architectures_SCOPUS.pdf; http://irep.iium.edu.my/101860/1/RasyidahMalariaYOLO5v2PDFexpress.pdf; http://irep.iium.edu.my/101860/; https://icsima.ieeemy-ims.org/22/program-schedule/; https://doi.org/10.1109/ICSIMA55652.2022.9928992

Malaria is a severe global public health issue that is caused by the bite of an infected mosquito. It is curable, but only with early detection and prompt, effective treatment. It may lead to severe conditions if it is not properly diagnosed and treated in its early stages. In the worst-case scenari...