Automatic polyp detection using SmartEndo-Net based on fusion feature pyramid network with mix-up edges

Young Jae Kim; Sohyun Byun; Chung il Ahn; Sangwook Cho; Kwang Gi Kim

doi:10.1117/12.2612651

4 April 2022 Automatic polyp detection using SmartEndo-Net based on fusion feature pyramid network with mix-up edges

Young Jae Kim, Sohyun Byun, Chung il Ahn, Sangwook Cho, Kwang Gi Kim

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120332S (2022) https://doi.org/10.1117/12.2612651
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Colonoscopy is essential for examining colorectal polyp or cancer. Examining colonoscopy has allowed for a reduction in the incidence and mortality of colorectal cancer through the detection and removal of polyps. However, missed polyp rate during colonoscopy has been reported as approximately 24% and intra- and inter-observer variability for polyp detection rates among endoscopists has been an issue. In this paper, we propose a real-time deep learning-based colorectal polyp detection system called SmartEndo-Net. To extract the polyp information, ResNet-50 is used in the backbone. To enable high-level feature fusion, extra mix-up edges in all level of the fusion feature pyramid network (FPN) are added. Fusion features are fed to a class and box network to produce object class and bounding box prediction. SmartEndo-Net is compared with Yolo-V3, SSD, and Faster R-CNN. SmartEndo-Net recorded sensitivity of 92.17% and proposed network was higher 7.96%, 6.78%, and 10.05% than Yolo-V3, SSD, and Faster R-CNN. SmartEndo-Net showed stable detection results regardless of polyp size, shape, and surrounding structures.

Conference Presentation

Citation Download Citation

Young Jae Kim, Sohyun Byun, Chung il Ahn, Sangwook Cho, and Kwang Gi Kim "Automatic polyp detection using SmartEndo-Net based on fusion feature pyramid network with mix-up edges", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120332S (4 April 2022); https://doi.org/10.1117/12.2612651

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