You Only Look Once (YOLO)-based object detectors have shown remarkable
accuracy for automated brain tumor detection. In this paper, we develop a novel
BGFG-YOLO architecture by incorporating Bi-level Routing Attention (BRA),
Generalized feature pyramid networks (GFPN), Forth detecting head, and
Generalized-IoU (GIoU) bounding box regression loss into YOLOv8. BGFG-YOLO
contains an attention mechanism to focus more on important features, and
feature pyramid networks to enrich feature representation by merging high-level
semantic features with spatial details. Furthermore, we investigate the effect
of different attention mechanisms and feature fusions, detection head
architectures on brain tumor detection accuracy. Experimental results show that
BGFG-YOLO gives a 3.4% absolute increase of mAP50 compared to YOLOv8x, and
achieves state-of-the-art on the brain tumor detection dataset Br35H. The code
is available at this https URL

该论文介绍了一种新颖的 BGFG-YOLO 架构，通过将 Bi-level Routing Attention（BRA）、Generalized feature pyramid networks（GFPN）、Forth detecting head 和 Generalized-IoU（GIoU）bounding box regression loss 结合到 YOLOv8 中，实现了对自动脑肿瘤检测的高准确性。实验结果显示，与 YOLOv8x 相比，BGFG-YOLO 在脑肿瘤检测数据集 Br35H 上的 mAP50 绝对增加了 3.4％，达到了 state-of-the-art 水平。