An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images - Dhevisha, Sukumarran; Khairunnisa, Hasikin; Anis Salwa, Mohd Khairuddin; Romano, Ngui; Wan Yusoff, Wan Sulaiman; Indra, Vythilingam; Paul Cliff, Simon Divis (BioMed Central Ltd., 2024)
Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease’s spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model—but with improved accuracy—for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector.