改进YOLOv9检测脑肿瘤，提升准确率。
SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis
- 在YOLOv9中引入SCConv模块优化特征提取
- 在两个数据集上分别提升mAP50 0.3%和0.5%
- 适合医学影像检测与临床辅助诊断研究者
脑肿瘤可导致神经功能障碍、认知及心理改变、颅内压升高和癫痫，严重威胁健康。单次检测（YOLO）系列在医学图像目标检测中表现出优异准确性。本文提出一种新型SCC-YOLO架构，将SCConv模块集成至YOLOv9。SCConv模块通过减少空间与通道冗余，提升卷积效率，增强图像特征学习能力。我们使用Br35H数据集和自建数据集Brain_Tumor_Dataset，对比不同注意力机制对脑肿瘤检测效果的影响。结果表明，SCC-YOLO在Br35H数据集上相较YOLOv9提升mAP50 0.3%，在自建数据集上提升0.5%，达到脑肿瘤检测当前最优性能。
原文摘要 · Abstract (English)
Brain tumors can lead to neurological dysfunction, cognitive and psychological changes, increased intracranial pressure, and seizures, posing significant risks to health. The You Only Look Once (YOLO) series has shown superior accuracy in medical imaging object detection. This paper presents a novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The SCConv module optimizes convolutional efficiency by reducing spatial and channel redundancy, enhancing image feature learning. We examine the effects of different attention mechanisms with YOLOv9 for brain tumor detection using the Br35H dataset and our custom dataset (Brain_Tumor_Dataset). Results indicate that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art performance in brain tumor detection.
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