标题： SCC-YOLO：一种用于辅助脑肿瘤诊断的改进型目标检测器 显示英文标题

标题： SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis

摘要： 脑肿瘤可能导致神经功能障碍、认知和心理状态的改变、颅内压升高以及癫痫的发生，从而对人类生命和健康构成重大风险。 You Only Look Once(YOLO)系列模型在医学影像的目标检测中表现出卓越的准确性。 在本文中，我们通过将SCConv注意力机制集成到YOLOv9中，开发了一种新颖的SCC-YOLO架构。 SCConv模块通过减少特征之间的空间和通道冗余来重建一个高效的卷积模块，从而增强图像特征的学习。 我们使用Br35H数据集和我们自制的数据集(Brain_Tumor_Dataset)研究了将不同的注意力机制与YOLOv9模型结合对脑肿瘤图像检测的影响。 实验结果表明，在Br35H数据集上，SCC-YOLO在mAp50方面相比YOLOv9提高了0.3%，而在我们自制的数据集上，SCC-YOLO相比YOLOv9提高了0.5%。 SCC-YOLO在脑肿瘤检测中达到了最先进的性能。 源代码可在以下地址获取：https://jihulab.com/healthcare-information-studio/SCC-YOLO/-/tree/master 显示英文摘要

摘要： Brain tumors can result in neurological dysfunction, alterations in cognitive and psychological states, increased intracranial pressure, and the occurrence of seizures, thereby presenting a substantial risk to human life and health. The You Only Look Once(YOLO) series models have demonstrated superior accuracy in object detection for medical imaging. In this paper, we develop a novel SCC-YOLO architecture by integrating the SCConv attention mechanism into YOLOv9. The SCConv module reconstructs an efficient convolutional module by reducing spatial and channel redundancy among features, thereby enhancing the learning of image features. We investigate the impact of intergrating different attention mechanisms with the YOLOv9 model on brain tumor image detection using both the Br35H dataset and our self-made dataset(Brain_Tumor_Dataset). Experimental results show that on the Br35H dataset, SCC-YOLO achieved a 0.3% improvement in mAp50 compared to YOLOv9, while on our self-made dataset, SCC-YOLO exhibited a 0.5% improvement over YOLOv9. SCC-YOLO has reached state-of-the-art performance in brain tumor detection. Source code is available at : https://jihulab.com/healthcare-information-studio/SCC-YOLO/-/tree/master