mlsanityData to 14 Oct 2026

RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection

arXiv 2307.16412 ↗First posted 31 Jul 2023cs.CV eess.SP stat.APcited by 5

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Abstract (arXiv)

With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection. However, the performance of using YOLO networks is scarcely investigated in brain tumor detection. We propose a novel YOLO architecture with Reparameterized Convolution based on channel Shuffle (RCS-YOLO). We present RCS and a One-Shot Aggregation of RCS (RCS-OSA), which link feature cascade and computation efficiency to extract richer information and reduce time consumption. Experimental results on the brain tumor dataset Br35H show that the proposed model surpasses YOLOv6, YOLOv7, and YOLOv8 in speed and accuracy. Notably, compared with YOLOv7, the precision of RCS-YOLO improves by 1%, and the inference speed by 60% at 114.8 images detected per second (FPS). Our proposed RCS-YOLO achieves state-of-the-art performance on the brain tumor detection task. The code is available at https://github.com/mkang315/RCS-YOLO.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 31 Jul 2023.

Builds on

18 of 34 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  5. 2014Going Deeper with Convolutionscited by 4,241
  6. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  7. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,200
  8. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  9. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  10. 2018Path Aggregation Network for Instance Segmentationcited by 798
  11. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  12. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 522
6 more
  1. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  2. 2022YOLOv6: A Single-Stage Object Detection Framework for Industrial Applicationscited by 275
  3. 2019CenterMask : Real-Time Anchor-Free Instance Segmentationcited by 201
  4. 2019An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detectioncited by 163
  5. 2023YOLOv6 v3.0: A Full-Scale Reloadingcited by 58
  6. 2022Designing Network Design Strategies Through Gradient Path Analysiscited by 32

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2023BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detectioncited by 7
  2. 2024LSM-YOLO: A Compact and Effective ROI Detector for Medical Detectioncited by 3
  3. 2025A Decade of You Only Look Once (YOLO) for Object Detection: A Reviewcited by 3
  4. 2024PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slicescited by 1
  5. 2024RepVGG-GELAN: Enhanced GELAN with VGG-STYLE ConvNets for Brain Tumour Detectioncited by 0