[1]白静毅,吴义熔,李小龙,等.基于改进YOLOv7的脑部MRI影像肿瘤检测算法[J].中国医学物理学杂志,2025,42(3):336-346.[doi:10.3969/j.issn.1005-202X.2025.03.009]
　BAI Jingyi,WU Yirong,et al.Algorithm for brain MRI tumor detection based on improved YOLOv7[J].Chinese Journal of Medical Physics,2025,42(3):336-346.[doi:10.3969/j.issn.1005-202X.2025.03.009]
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基于改进YOLOv7的脑部MRI影像肿瘤检测算法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第3期
页码:
336-346
栏目:
医学影像物理
出版日期:
2025-03-20

文章信息/Info

Title:
Algorithm for brain MRI tumor detection based on improved YOLOv7
文章编号:
1005-202X(2025)03-0336-11
作者:
白静毅1; 2; 吴义熔1; 2; 3; 李小龙1; 2; 孙水发1; 2; 4
1.三峡大学计算机与信息学院/水电工程智能视觉监测湖北省重点实验室，湖北 宜昌 443002；2.三峡大学计算机与信息学院/智慧医疗宜昌市重点实验室，湖北 宜昌 443002；3.北京师范大学人文和社会科学高等研究院，广东 珠海 519087；4.杭州师范大学信息科学与技术学院，浙江 杭州 311121
Author(s):
BAI Jingyi1;  2;  WU Yirong1;  2;  3;  LI Xiaolong1;  2;  SUN Shuifa1;  2;  4
1. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering/College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China; 2. Yichang Key Laboratory of Intelligent Medicine/College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China; 3. Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai 519087, China; 4. School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
关键词:
脑肿瘤; YOLOv7; 部分卷积; 三维空间注意力; 动态注意力
Keywords:
brain tumor;  YOLOv7;  partial convolution;  three-dimensional spatial attention;  dynamic attention
分类号:
R318
DOI:
10.3969/j.issn.1005-202X.2025.03.009
文献标志码:
A
摘要:
脑部 MRI 影像数据具有数据量大、易受噪声和伪影干扰等特点,这些特点对种类、形状、边界既相似又复杂多变的 脑肿瘤检测和分析提出了速度和准确度提升的挑战。基于此,在 YOLOv7 算法基础上提出一系列改进方法来提高检测的 精度和速度。首先在特征提取阶段使用部分卷积 PConv,以降低模型的参数量,提高整体检测速度。其次针对脑肿瘤复 杂多变的特点,在特征提取时引入三维空间注意力机制 SimAM,以提高模型对重要影像特征的关注。最后用 WIoU 替换 原 IoU 损失函数,在边界框回归时提高对普通质量锚框的关注,以进一步提高检测精度。通过在公开的两个脑肿瘤数据 集 Brain_Tumor 和 Glioma_of_test 上进行实验,改进后的模型 mAP 检测精度分别为 96.9% 和 92.8%,相比 YOLOv7 原模型 提高 1.4% 和 2.4%;FPS 每秒处理的图像数分别为 162.7 和 158.1,相比 YOLOv7 原模型提高 6.4 和 18.2,可以更为有效地检 测脑部 MRI 影像中的脑肿瘤。
Abstract:
Brain MRI data is characterized by large volumes and susceptibility to noise and artifacts, which pose significant challenges of improving the speed and accuracy of brain tumor detection and analysis due to the tumors’ diverse types, shapes, and boundaries that are both similar and highly variable. Therefore, a series of improvements based on YOLOv7 algorithm are proposed for enhancing detection precision and speed: (1) employing partial convolution during feature extraction to reduce the model’s parameters and improve overall detection speed; (2) in light of the complex variability of brain tumors, introducing a three-dimensional spatial attention mechanism during feature extraction to enhance the model’s focus on critical image features; (3) replacing the original IoU loss function with WIoU to increase the attention to mediumquality anchor boxes during bounding box regression for further improving detection accuracy. Experiments conducted on two public brain tumor datasets, Brain_Tumor and Glioma_of_test, show that the improved model achieves mAP of 96.9% and 92.8%, which are 1.4% and 2.4% higher than the original YOLOv7 model, and the frames per second reach 162.7 and 158.1, showing improvements of 6.4 and 18.2, respectively. These enhancements enable more effective detection of brain tumors in MRI images.

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备注/Memo

备注/Memo:
【收稿日期】2024-11-28 【基金项目】国家社会科学基金(20BTQ066) 【作者简介】白 静 毅 ,硕 士 研 究 生 ,研 究 方 向 :智 慧 医 疗 ,E-mail: 1214518574@qq.com 【通信作者】吴义熔,教授,博士生导师,研究方向:医学大数据和精确 医学,E-mail: yrwu@bnu.edu.cn
更新日期/Last Update: 2025-03-26