RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection

Ming Kang; Chee-Ming Ting; Fung Fung Ting; Raphaël C.W. Phan

doi:10.1007/978-3-031-43901-8_57

RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection

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

Malaysia Sch of Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

28 Citations (Scopus)

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.

Original language	English
Title of host publication	26th International Conference Vancouver, BC, Canada, October 8–12, 2023 Proceedings, Part IV
Editors	Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	600-610
Number of pages	11
ISBN (Electronic)	9783031439018
ISBN (Print)	9783031439001
DOIs	https://doi.org/10.1007/978-3-031-43901-8_57
Publication status	Published - 2023
Event	Medical Image Computing and Computer-Assisted Intervention 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 12 Oct 2023 Conference number: 26th https://link.springer.com/book/10.1007/978-3-031-43901-8 (Proceedings) https://conferences.miccai.org/2023/en/ (Website)

Publication series

Name	Lecture Notes in Computer Science
Volume	14223
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Medical Image Computing and Computer-Assisted Intervention 2023
Abbreviated title	MICCAI 2023
Country/Territory	Canada
City	Vancouver
Period	8/10/23 → 12/10/23
Internet address	https://link.springer.com/book/10.1007/978-3-031-43901-8 (Proceedings) https://conferences.miccai.org/2023/en/ (Website)

Keywords

Channel shuffle
Computation efficiency
Medical image detection
Reparameterized convolution
YOLO

Access to Document

10.1007/978-3-031-43901-8_57

Cite this

Kang, M., Ting, C.-M., Ting, F. F., & Phan, R. C. W. (2023). RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), 26th International Conference Vancouver, BC, Canada, October 8–12, 2023 Proceedings, Part IV (pp. 600-610). (Lecture Notes in Computer Science; Vol. 14223). Springer. https://doi.org/10.1007/978-3-031-43901-8_57

Kang, Ming ; Ting, Chee-Ming ; Ting, Fung Fung et al. / RCS-YOLO : A fast and high-accuracy object detector for brain tumor detection. 26th International Conference Vancouver, BC, Canada, October 8–12, 2023 Proceedings, Part IV. editor / Hayit Greenspan ; Anant Madabhushi ; Parvin Mousavi ; Septimiu Salcudean ; James Duncan ; Tanveer Syeda-Mahmood ; Russell Taylor. Cham Switzerland : Springer, 2023. pp. 600-610 (Lecture Notes in Computer Science).

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title = "RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection",

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 = "Ming Kang and Chee-Ming Ting and Ting, {Fung Fung} and Phan, {Rapha{\"e}l C.W.}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; Medical Image Computing and Computer-Assisted Intervention 2023, MICCAI 2023 ; Conference date: 08-10-2023 Through 12-10-2023",

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booktitle = "26th International Conference Vancouver, BC, Canada, October 8–12, 2023 Proceedings, Part IV",

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Kang, M, Ting, C-M , Ting, FF & Phan, RCW 2023, RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection. in H Greenspan, A Madabhushi, P Mousavi, S Salcudean, J Duncan, T Syeda-Mahmood & R Taylor (eds), 26th International Conference Vancouver, BC, Canada, October 8–12, 2023 Proceedings, Part IV. Lecture Notes in Computer Science, vol. 14223, Springer, Cham Switzerland, pp. 600-610, Medical Image Computing and Computer-Assisted Intervention 2023, Vancouver, British Columbia, Canada, 8/10/23. https://doi.org/10.1007/978-3-031-43901-8_57

RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection. / Kang, Ming; Ting, Chee-Ming ; Ting, Fung Fung et al.
26th International Conference Vancouver, BC, Canada, October 8–12, 2023 Proceedings, Part IV. ed. / Hayit Greenspan; Anant Madabhushi; Parvin Mousavi; Septimiu Salcudean; James Duncan; Tanveer Syeda-Mahmood; Russell Taylor. Cham Switzerland: Springer, 2023. p. 600-610 (Lecture Notes in Computer Science; Vol. 14223).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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AU - Ting, Chee-Ming

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N2 - 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.

AB - 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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KW - Computation efficiency

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Kang M, Ting CM , Ting FF , Phan RCW. RCS-YOLO: A fast and high-accuracy object detector for brain tumor detection. In Greenspan H, Madabhushi A, Mousavi P, Salcudean S, Duncan J, Syeda-Mahmood T, Taylor R, editors, 26th International Conference Vancouver, BC, Canada, October 8–12, 2023 Proceedings, Part IV. Cham Switzerland: Springer. 2023. p. 600-610. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-43901-8_57