基于改进YOLO模型的脑肿瘤病灶区域检测

doi:10.11896/jsjkx.241000166

摘要/Abstract

摘要： 针对传统人工检测在脑肿瘤阅片中易受主观因素影响而出现误诊或漏诊的问题,结合脑肿瘤图像的特点,提出一种改进YOLO模型以实现对脑肿瘤病灶区域的智能检测。针对脑肿瘤病灶区域形状不规则的特点,引入可变形卷积,使得网络能自适应复杂病灶形态,提升不规则病灶的特征提取能力。同时,通过嵌入结合了全局多头注意力、局部注意力和通道注意力的全局注意力机制,使网络在关注病灶区域细微特征的同时,降低图像复杂背景对病灶区域特征提取的负面影响,以获得更高的识别准确率。此外,针对脑肿瘤数据集标注中预测框不一定精准的实际情况,采用改进Wise-IoU代替原有的CIoU损失函数,以应对人工标注不精准的问题。在脑肿瘤数据集Brain Tumor Detection上的对比实验结果表明,所提出的模型相比于原始模型,精度提高了5.9%。

关键词: YOLO, 医学图像检测, 脑肿瘤, 注意力机制, 可变形卷积

Abstract: Aiming at the problem that traditional manual detection is easily affected by subjective factors leading to misdiagnosis or omission in brain tumour reading,an improved YOLO model is proposed for intelligent detection of brain tumour foci region by combining the characteristics of brain tumour images.Aiming at the irregular shape of brain tumour lesion regions,deformable convolution is introduced to make the network adaptive to complex lesion morphology and improve the feature extraction ability of irregular lesions.Meanwhile,by embedding a global attention mechanism that combines global multi-attention,local attention and channel attention,the network focuses on the subtle features of the lesion region while reducing the negative impact of the complex background of the image on the feature extraction of the lesion region in order to obtain a higher recognition accuracy.In addition,for the actual situation that the prediction frames in the brain tumour dataset annotation are not necessarily accurate,the improved Wise-IoU is used instead of the original CIoU loss function to adapt to the problem of inaccurate manual annotation.The results of comparison experiments on the brain tumour dataset Brain Tumor Detection show that the proposed model improves the accuracy by 5.9％compared to the original model.

Key words: YOLO, Medical image detection, Brain tumor, Attention mechanism, Deformable convolution

中图分类号:

TP391

荣昌达, 殷继彬. 基于改进YOLO模型的脑肿瘤病灶区域检测[J]. 计算机科学, 2025, 52(11A): 241000166-8. https://doi.org/10.11896/jsjkx.241000166

RONG Changda, YIN Jibin. Detection of Brain Tumor Lesion Areas Based on Improved YOLO Model[J]. Computer Science, 2025, 52(11A): 241000166-8. https://doi.org/10.11896/jsjkx.241000166

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