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

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14223))

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Abstract

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.

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Author information

Authors and Affiliations

  1. School of Information Technology, Monash University, Malaysia Campus, Subang Jaya, Malaysia

    Ming Kang, Chee-Ming Ting, Fung Fung Ting & Raphaël C.-W. Phan

Authors
  1. Ming Kang
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  2. Chee-Ming Ting
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  3. Fung Fung Ting
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  4. Raphaël C.-W. Phan
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Corresponding author

Correspondence to Chee-Ming Ting .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen's University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Kang, M., Ting, CM., Ting, F.F., Phan, R.CW. (2023). RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_57

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  • DOI: https://doi.org/10.1007/978-3-031-43901-8_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43900-1

  • Online ISBN: 978-3-031-43901-8

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