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

Table 4 Network structure of YOLOv4. Network structure of the YOLOv4 model YOLOv4 melsNumber of layersTypeCSPDarkNet53 without the fully connected layer0 1–7 8CBL Res Conv9Route10–202Res21Conv22Route23–518Res52Conv53Route54–828Res83Conv84Route85–1014Res102Conv103RouteFeature fusion layer and output layer104–107 108–1134CBL SPP114–1174CBL118Up-sample119Route120Conv121Route122–1276CBL128Up-sample129Route130CBL131Route132–1376CBL138–139CBL +YOLO140Route141CBL142Route143–1486CBL149–150Conv +YOLO151Route152CBL153Route154–1596CBL160–161Conv +YOLO Head:The main function is to locate the bounding boxes and classify the objects of interest.The coordinates and the scores of every bounding box are generated YOLO You Only Look Once (model), CBL Convolutional,Batch normalisation,and Leaky-ReLU (Feature extractor), RES residual block