Objective To explore the application of improved YOLOv7 algorithm for the automatic detection and classification of different types of blood cells in blood cell images,to improve the accuracy of blood cell recognition and classification.Methods The swin transformer module was integrated into the YOLOv7,coupled with the adoption of the weighted bidirectional feature pyramid network structure,which enabled the network to acquire and propagate richer feature information.The SCYLLA-IoU loss function was employed to replace the conventional complete IoU loss,resulting in more precise target bounding box localization.Results Experimental evaluations conducted on the BCCD blood cell dataset showcased that the improved YOLOv7 model achieved recognition accuracies of 89.3%,98.5%,and 91.5%for red blood cells,white blood cells,and platelets,respectively.The mean average precision reached 93.1%,which demonstrated a 2.6%improvement over the original YOLOv7 model.Comparative analysis with other published artificial intelligence-based blood cell detection algorithms revealed the superior accuracy of the proposed algorithm.Conclusion The improved YOLOv7 model proves effective for blood cell recognition and classification tasks,which provides significant value in the domain of blood cell detection.
维普
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