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27 March 2024 Colorectal white light filter object detection based on improved YOLOv5
Jun Long Li, Jun Ru Liang, Liang Qi Ren, Kun Peng Zhang
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Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310513 (2024) https://doi.org/10.1117/12.3026445
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China
Abstract
As a device for detecting colopathy, white light endoscopy still suffers from missed diagnoses and misdiagnoses. For increasing the diagnostic rate of colopathy sick persons, our work proposes a new diagnoses model for pathological changes based on an improved YOLOv5 algorithm based on the SwinTransformer framework. In this diagnostic model, colopathy is divided into three categories: polyp of the colon, adenoma of the colon, and carcinomas. The SwinStage structure, as well as the channel attention mechanism and spatial attention mechanism, are incorporated to fully extract features of the map and the relationship between feature maps, thereby improving the result of object detection. Experimental outcomes demonstrate that the proposed pathological tissue diagnoses model has a more excellent pathological tissue detection ability, the mAP@0.5 and mAP@0.5:0.95 can attain 0.915 and 0.717.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jun Long Li, Jun Ru Liang, Liang Qi Ren, and Kun Peng Zhang "Colorectal white light filter object detection based on improved YOLOv5", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310513 (27 March 2024); https://doi.org/10.1117/12.3026445
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KEYWORDS
Object detection
Tissues
Colon
Diagnostics
Feature extraction
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