English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Colon cancer is expected to become the second leading cause of cancer deathin the United States in 2023. Although colonoscopy is one of the most effectivemethods for early prevention of colon cancer, up to 30% of polyps may be missedby endoscopists, thereby increasing patients' risk of developing colon cancer.Though deep neural networks have been proven to be an effective means ofenhancing the detection rate of polyps. However, the variation of polyp sizebrings the following problems: (1) it is difficult to design an efficient andsufficient multi-scale feature fusion structure; (2) matching polyps ofdifferent sizes with fixed-size anchor boxes is a hard challenge. Theseproblems reduce the performance of polyp detection and also lower the model'straining and detection efficiency. To address these challenges, this paperproposes a new model called YOLO-OB. Specifically, we developed a bidirectionalmultiscale feature fusion structure, BiSPFPN, which could enhance the featurefusion capability across different depths of a CNN. We employed the ObjectBoxdetection head, which used a center-based anchor-free box regression strategythat could detect polyps of different sizes on feature maps of any scale.Experiments on the public dataset SUN and the self-collected colon polypdataset Union demonstrated that the proposed model significantly improvedvarious performance metrics of polyp detection, especially the recall rate.Compared to the state-of-the-art results on the public dataset SUN, theproposed method achieved a 6.73% increase on recall rate from 91.5% to 98.23%.Furthermore, our YOLO-OB was able to achieve real-time polyp detection at aspeed of 39 frames per second using a RTX3090 graphics card. The implementationof this paper can be found here: https://github.com/seanyan62/YOLO-OB.

YOLO-OB: An improved anchor-free real-time multiscale colon polyp detector in colonoscopy