Simulation of Melanoma Image Detection Based on YOLOv5
To address the problems of low detection accuracy and too much human subjectivity in melanoma dis-ease in clinical practice,an improved YOLOv5 target detection model BiC-YOLOv5 is proposed.Firstly,a bidirec-tional feature extraction network BiFPN-L3 was designed to replace the feature extraction network FPN in the original model,and a multi-scale feature fusion was used to extract features at different resolutions.Second,a CBAM attention module was fused in the backbone network,and a C3CBAM module was designed to capture feature information from both channel and space levels to improve detection accuracy;Finally,the DIOU_loss loss function was used to further improve the detection accuracy of the model.Through simulation comparison,the mAP value of BiC-YOLOv5 reached 95.2%,which is an improvement of 5.2%in accuracy,4.9%in recall,and 5.8%in mAP value compared to the o-riginal YOLOv5 model.This can effectively assist clinical medicine in diagnosing melanoma.
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