# BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection

arXiv: https://arxiv.org/abs/2309.12585 · HTML: https://mlsanity.com/paper/2309.12585/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 23 of 23)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [BiFormer: Vision Transformer with Bi-Level Routing Attention](https://mlsanity.com/paper/2303.08810/) (2023)
- [Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection](https://mlsanity.com/paper/2011.12885/) (2020)
- [Focal and Efficient IOU Loss for Accurate Bounding Box Regression](https://mlsanity.com/paper/2101.08158/) (2021)
- [SIoU Loss: More Powerful Learning for Bounding Box Regression](https://mlsanity.com/paper/2205.12740/) (2022)
- [DAMO-YOLO : A Report on Real-Time Object Detection Design](https://mlsanity.com/paper/2211.15444/) (2022)
- [Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism](https://mlsanity.com/paper/2301.10051/) (2023)
- [GiraffeDet: A Heavy-Neck Paradigm for Object Detection](https://mlsanity.com/paper/2202.04256/) (2022)
- [AFPN: Asymptotic Feature Pyramid Network for Object Detection](https://mlsanity.com/paper/2306.15988/) (2023)
- [RFAConv: Receptive-Field Attention Convolution for Improving Convolutional Neural Networks](https://mlsanity.com/paper/2304.03198/) (2023)
- [RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection](https://mlsanity.com/paper/2307.16412/) (2023)
- [Improved YOLOv8 Detection Algorithm in Security Inspection Image](https://mlsanity.com/paper/2308.06452/) (2023)

## Built on by (most cited first, 7 of 7)

- [SOD-YOLOv8 -- Enhancing YOLOv8 for Small Object Detection in Traffic Scenes](https://mlsanity.com/paper/2408.04786/) (2024)
- [MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalities](https://mlsanity.com/paper/2511.20650/) (2025)
- [PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices](https://mlsanity.com/paper/2410.21822/) (2024)
- [SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis](https://mlsanity.com/paper/2501.03836/) (2025)
- [Improving Generalization Performance of YOLOv8 for Camera Trap Object Detection](https://mlsanity.com/paper/2412.14211/) (2024)
- [Efficient SAR Vessel Detection for FPGA-Based On-Satellite Sensing](https://mlsanity.com/paper/2507.04842/) (2025)
- [Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation](https://mlsanity.com/paper/2510.10679/) (2025)

## Abstract (arXiv)

You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection. In this paper, we develop a novel BGF-YOLO architecture by incorporating Bi-level routing attention, Generalized feature pyramid networks, and Fourth detecting head into YOLOv8. BGF-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 BGF-YOLO gives a 4.7% 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 https://github.com/mkang315/BGF-YOLO.