计算技术与自动化2026，Vol.45Issue(1)：18-25,8.DOI:10.16339/j.cnki.jsjsyzdh.202601003
基于改进YOLOv8的脑肿瘤图像检测算法
Brain Tumor Image Detection Algorithm Based on Improved YOLOv8
摘要
Abstract
Brain tumors are a severe threat to human health,and early detection is crucial for improving treatment out-comes.To address the issue of insufficient detection accuracy in existing brain tumor detection algorithms under complex backgrounds or with blurred tumor boundaries,this paper proposes an improved YOLOv8-based brain tumor detection algo-rithm.The algorithm introduces a coordinate attention mechanism to enhance feature focus on tumor regions;employs depthwise separable convolution in the backbone network to reduce computational complexity and improve feature extraction efficiency;and incorporates the bottleneck transformer module to strengthen global information modeling.Experiments were conducted on the brain tumor detection dataset,and the results show that the detection accuracy reached 93％,an improve-ment of 1.1％over the original algorithm.mAP0.5 and mAP0.5∶0.95 were increased by 2.1％and 1.7％,respectively.The experimental results demonstrate that the improved algorithm exhibits significant advantages in brain tumor detection tasks,providing more accurate and efficient support for medical image-assisted diagnosis.关键词
脑肿瘤检测/YOLOv8/协调注意力机制/深度可分离卷积/Bottleneck TransformerKey words
brain tumor detection/YOLOv8/coordinate attention/depthwise separable convolution/Bottleneck Trans-former分类
信息技术与安全科学引用本文复制引用
郑泽毅,曹嘉璇,王家琪,邹北骥,郭纯,刘青萍..基于改进YOLOv8的脑肿瘤图像检测算法[J].计算技术与自动化,2026,45(1):18-25,8.基金项目
国家重大科技专项项目(2018AAA0102100) （2018AAA0102100）
2023年湖南省学位与研究生教学改革研究项目(2023JGYB151) （2023JGYB151）
湖南中医药大学第一附属医院中医药传承创新专项重点项目(2024XYLH346) （2024XYLH346）
湖南省普通高等学校教学改革研究项目(HNJG—20230527) （HNJG—20230527）
2024年湖南中医药大学研究生科研创新项目(2024CX192) （2024CX192）
