Table 2 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

Table 2 Performance comparison of the proposed YOLO model with those reported other published works AuthorsTechniquesDatasets/Blood smearsPlasmodium parasitesmAP (%)Yang F et al. [30]Modified YOLOv2Self-collected 2567 thin blood smear imagesP.vivax79.22Koirala.et al. [33]Modified YOLOv3 and YOLOV4 TinySelf-collected 3885 thick blood smear imagesP.falciparum94.07Sukumarran et al. [32]YOLOv4MP-IDB and 236 images from MRC-UNIMAS SarawakP.falciparum P.vivax84.04P.ovaleP.malariaeP.knowlesiAbdurahman et al. [62]Modified YOLOv3 and YOLOv4Publicly available 1182 thick blood smear imagesP.falcipraum89.73Present study (YOLOv4-RC3_4)Modified YOLOv4MP-IDB The malaria parasite image database and new dataset from MRC-UNIMAS SarawakP.falciparum90.07P.vivaxP.ovaleP.malariaeP.knowlesi mAP Mean average precision, MP-IDB Malaria Parasite Image Database, MRC-UNIMAS Malaria Research Centre-Universiti Malaysia Sarawak, YOLO You Only Look Once object detection algorithm