Algorithm for brain MRI tumor detection based on improved YOLOv7
10.3969/j.issn.1005-202X.2025.03.009
- VernacularTitle:基于改进YOLOv7的脑部MRI影像肿瘤检测算法
- Author:
Jingyi BAI
1
;
Yirong WU
;
Xiaolong LI
;
Shuifa SUN
Author Information
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;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
- Publication Type:Journal Article
- Keywords:
brain tumor;
YOLOv7;
partial convolution;
three-dimensional spatial attention;
dynamic attention
- From:
Chinese Journal of Medical Physics
2025;42(3):336-346
- CountryChina
- Language:Chinese
-
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 medium-quality 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.
