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

Parasites & Vectors

Table 7 Detection of infected red blood cells on Dataset A using the original YOLOv4 and modified models

Modifications	Model	Precision (%)	Recall rate (%)	F1-score (%)	mAP (%)	Training time (h)	Inference time (per image) (ms)	B-FLOPS	Size (MB)
Original	YOLOv4	84	95	89	93.87	48	726.66	59.57	244.40
Residual block pruning	YOLOv4-RC3	84	92	88	91.65	35	678.53	47.59	242.40
	YOLOv4-RC4	83	92	87	92.84	37	703.82	51.21	233.20
	YOLOv4-RC5	85	89	87	92.47	37	704.48	57.61	222.10
	YOLOv4-RC3_4	83	89	86	88.09	32	676.18	37.35	221.50
	YOLOv4-RC3_5	77	77	77	76.56	32.5	680.01	45.64	220.4
Backbone replacement	YOLOv4- ResNet-50L	70	84	76	79.70	28	719.50	37.33	209.30
Backbone replacement	YOLOv4-ResNet-50 M	74	86	80	81.43	28	884.82	37.33	209.30

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

ISSN: 1756-3305