Abstract
Object detection algorithms particularly those based on YOLO have demonstrated remarkable efficiency in balancing speed and accuracy. However, their application in brain tumour detection remains underexplored. This study proposes RepVGG-GELAN, a novel YOLO architecture enhanced with RepVGG, a reparameterized convolutional approach for object detection tasks particularly focusing on brain tumour detection within medical images. RepVGG-GELAN leverages the RepVGG architecture to improve both speed and accuracy in detecting brain tumours. Integrating RepVGG into the YOLO framework aims to achieve a balance between computational efficiency and detection performance. This study includes a spatial pyramid pooling-based Generalized Efficient Layer Aggregation Network (GELAN) architecture which further enhances the capability of RepVGG. Experimental evaluation conducted on a brain tumour dataset demonstrates the effectiveness of RepVGG-GELAN surpassing existing RCS-YOLO in terms of precision and speed. Specifically, RepVGG-GELAN achieves an increased precision of 4.91% and an increased AP50 of 2.54% over the latest existing approach while operating at 240.7 GFLOPs. The proposed RepVGG-GELAN with GELAN architecture presents promising results establishing itself as a state-of-the-art solution for accurate and efficient brain tumour detection in medical images. The implementation code is publicly available at this https URL.
Abstract (translated)
物体检测算法，尤其是基于YOLO的算法，已经在平衡速度和精度方面取得了显著的效率。然而，在肿瘤检测应用中，它们的应用仍然没有被充分利用。这项研究提出了RepVGG-GELAN，一种新颖的YOLO架构，通过在RepVGG上进行重新参数化卷积，特别是在医学图像中的肿瘤检测。RepVGG-GELAN利用RepVGG架构来提高检测肿瘤的速度和准确性。将RepVGG集成到YOLO框架中旨在实现计算效率和检测性能的平衡。本研究包括一个基于空间金字塔池化的全局 efficient层聚合网络（GELAN）架构，进一步增强了RepVGG的检测能力。在肿瘤数据集上进行的实验评估表明，RepVGG-GELAN在准确性和速度方面超过了现有的RCS-YOLO。具体来说，RepVGG-GELAN在最新的现有方法的基础上实现了4.91%的增加的准确性和2.54%的增加的AP50。所提出的RepVGG-GELAN与GELAN架构相结合，为准确且高效的肿瘤检测在医学图像中提供了有前景的解决方案。实现代码可在此链接的公开URL中获取。
URL
https://arxiv.org/abs/2405.03541
