基于改进YOLOv7的皮肤良恶性病变检测算法  

Detection Algorithm of Benign and Malignant Skin Lesions Based on Improved YOLOv7

作　　者：陈杰[1] 张梅[1] 张一帆 Chen Jie;Zhang Mei;Zhang Yifan(School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan,Anhui 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《黑龙江工业学院学报（综合版）》2024年第5期70-79,共10页Journal of Heilongjiang University of Technology(Comprehensive Edition)

基　　金：国家自然科学基金资助项目(项目编号:52374154)。

摘　　要：针对在皮肤黑色素瘤目标检测中出现精度低、漏检率高等问题,提出了一种基于改进YOLOv7的皮肤良恶性病变检测算法。首先,在头部网络中,采用GSConv卷积结构代替网络模型中的普通卷积结构,降低模型权重,提升对皮肤良恶性病变的检测精度;其次,在骨干网络中融合SE(squeeze and excitation)注意力机制,以提升对皮肤病变的特征提取能力;最后,利用EIOU损失函数代替CIOU损失函数,进一步提高检测精度。实验表明,经过改进后的YOLOv7模型在皮肤良恶性病变数据集上的平均检测精度均值@0.5(mean average precision@0.5,mAP@0.5)达到了90.9%、精确度为89.1%,较YOLOv7模型平均检测精度均值提升了4.4%、精确度提升了11.0%、运行参数较YOLOv7降低了11.6%。所提出的改进的YOLOv7模型能够很好地识别良恶性病变,能够更好地辅助医生诊断。In response to the problems of low accuracy and high missed detection rate in target detection of skin melanoma,this paper proposes a skin benign and malignant lesions detection algorithm based on improved YOLOv7.Firstly,in the head network,the GSConv convolution structure is employed as a substitute for the conventional convolution structure in the network model.This substitution reduces the model′s weight while enhancing the detection accuracy of benign and malignant skin lesions.Secondly,the SE(squeeze and excitation) attention mechanism is fused in the backbone network to enhance the feature extraction ability of skin lesions.Lastly,the EIOU loss function is utilized in place of the CIOU loss function to further enhance the accuracy of detection.Experimental results demonstrate that the enhanced YOLOv7 model achieves an average detection precision mean(mAP@0.5) of 90.9% and an accuracy of 89.1% on the dataset of skin benign and malignant lesions.The proposed improved YOLOv7 model demonstrates a significant advancement in performance,with a 4.4% increase in average detection precision mean and 11.0% improvement in accuracy compared to the baseline YOLOv7 model.Additionally,the operating parameters have been reduced by 11.6% in comparison to YOLOv7.This enhanced model effectively identifies benign and malignant lesions,offering valuable assistance to doctors in the diagnosis process.

关 键 词：皮肤病变 目标检测 YOLOv7 GSConv 注意力机制 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]