An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Parasites & Vectors

Table 8 Detection of infected RBC on Dataset B using the original YOLOv4 and modified models

Modifications	Model	Precision (%)	Recall rate (%)	F1-score (%)	mAP (%)	Inference time (ms)	B-FLOPS	Size (MB)
Original	YOLOv4	61	86	72	81.43	905.65	59.57	244.40
Residual Block pruning	YOLOv4-RC3	50	95	66	88.91	695.54	47.59	242.40
	YOLOv4-RC4	50	91	65	85.20	695.98	51.21	233.20
	YOLOv4-RC5	61	93	73	89.84	731.43	57.61	222.10
	YOLOv4-RC3_4	59	96	74	90.70	684.93	37.35	221.50
	YOLOv4-RC3_5	59	93	72	88.09	690.53	45.64	220.40
Backbone replacement	YOLOv4- ResNet-50L	54	83	65	76.95	892.80	37.33	209.30
Backbone replacement	YOLOv4-ResNet-50 M	65	81	72	78.96	905.39	37.33	209.30

B-FLOPS Billion floating-point operations, F1-score balance between precision and recall, mAP mean average precision

ISSN: 1756-3305