基于YOLOv8改进的脑癌检测算法  

Enhanced Brain Cancer Detection Algorithm Based on YOLOv8

作　　者：王喆 赵慧俊 谭超[1] 李骏 申冲 WANG Zhe;ZHAO Huijun;TAN Chao;LI Jun;SHEN Chong(School of Mechanical Engineering,North University of China,Taiyuan 030051,China;School of Instruments and Electronics,North University of China,Taiyuan 030051,China;State Key Laboratory of Dynamic Measurement Technology of Shanxi Province,Taiyuan 030051,China)

机构地区：[1]中北大学机械工程学院,太原030051 [2]中北大学仪器与电子学院,太原030051 [3]山西省省部共建动态测试技术国家重点实验室,太原030051

出　　处：《计算机科学》2024年第S02期444-450,共7页Computer Science

基　　金：光电信息控制和安全技术重点实验室基金(2021JCJQLB055010)。

摘　　要：自动检测脑部肿瘤在磁共振成像中的位置是一个复杂、繁重的任务,需要耗费大量时间和资源。传统识别方案经常出现误解、遗漏和误导的情况,从而影响患者的治疗进度,对患者的生命安全产生影响。为了进一步提高鉴定的效果,引入了4项关键改进措施。首先,采用了高效的多尺度注意力EMA(Efficient Multi-scale Attention),这种方法既可以对全局信息进行编码,也可以对信息进行重新校准,同时通过并行的分支输出特征进行跨维度的交互,使信息进一步聚合。其次,引入了BiFPN(Bidirectional Feature Pyramid Network)模块,并对其结构进行改进,以便缩短每一次检测所需要的时间,同时提升图像识别效果。然后采用MDPIoU损失函数和Mish激活函数进行改进,进一步提高检测的准确度。最后进行仿真实验,实验结果表明,改进的YOLOv8算法在脑癌检测中的精确率、召回率、平均精度均值均有提升,其中Precision提高了4.48%,Recall提高了2.64%,mAP@0.5提高了2.6%,mAP@0.5:0.9提高了7.0%。Automatically detecting the location of brain tumors in magnetic resonance imaging is a complex and laborious task that requires a lot of time and resources.Traditional identification schemes often misunderstand,omit,and mislead,affecting the progress of patient treatment and posing a risk to patient safety.To further improve the identification and appraisal results,this paper introduces four key improvement measures.Firstly,an efficient multi-scale attention EMA is adopted,which can encode global information,recalibrate information,and further aggregate information through parallel branch output features for cross-dimensional interaction.Secondly,the BiFPN(Bidirectional Feature Pyramid Network)module is introduced to shorten the time required for each detection and improve image recognition results.Then,the MDPIoU loss function and Mish activation function are improved to further enhance detection accuracy.Finally,simulation experiments are conducted for verification.The experimental results show that the improved YOLOv8 algorithm has improved precision,recall,and mean average precision(mAP)in brain cancer detection.Among them,precision,recall,mAP@0.5 and mAP@0.5:0.9increases by 4.48%,2.64%,2.6% and 17.0% respectively.

关 键 词：YOLOv8 脑癌 Efficient Multi-Scale Attention模块 Bidirectional Feature Pyramid Network结构 Missed Softplus with Identity Shortcut激活函数 Minimum Point Distance Intersection over Union损失函数 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]