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Related Concept Videos

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

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This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
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A colonic polyps detection algorithm based on an improved YOLOv5s.

Jianjun Li1,2, Jinhui Zhao3, Yifan Wang1

  • 1College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou, 310018, China.

Scientific Reports
|February 26, 2025
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Summary
This summary is machine-generated.

Early detection of colon cancer polyps is crucial. Improved AI algorithms, like YOLOv5s with attention mechanisms and feature fusion, enhance polyp detection accuracy in colonoscopy images, aiding early diagnosis.

Keywords:
Convolutional neural networkDigestive endoscopyFeature fusionTarget detection

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Oncology

Background:

  • Colon cancer is a leading cause of mortality, with early detection via endoscopy being the most effective prevention strategy.
  • Advancements in artificial intelligence (AI) offer potential for improving the accuracy and efficiency of colonic polyp detection in colonoscopy images.
  • Current target detection algorithms require further enhancement for optimal clinical efficacy in identifying precancerous polyps.

Purpose of the Study:

  • To enhance the YOLOv5s algorithm for improved detection of colonic polyps in endoscopic images.
  • To investigate the efficacy of integrating attention mechanisms (SE) and feature fusion (BiFPN) into the YOLOv5s architecture.
  • To validate the performance of the improved models against existing algorithms using clinical and public datasets.

Main Methods:

  • Modified the C3 module of YOLOv5s to C3SE by incorporating the Squeeze-and-Excitation (SE) attention mechanism.
  • Implemented weighted bi-directional feature pyramid network (BiFPN) for enhanced fusion of higher-level features.
  • Evaluated the improved YOLOv5s models (YOLOv5s+BiFPN, YOLOv5s-1st-2nd-C3SE, YOLOv5s+SEBiFPN) against other algorithms on a new colonic polyp dataset.

Main Results:

  • The YOLOv5s+BiFPN and YOLOv5s-1st-2nd-C3SE models showed improved detection capabilities compared to the base YOLOv5 algorithm.
  • The YOLOv5s+SEBiFPN model demonstrated substantial performance gains over the standard YOLOv5s algorithm.
  • Key performance indicators including mAP (mean Average Precision), accuracy, and recall were used for comparison.

Conclusions:

  • The integration of SE attention and BiFPN feature fusion significantly enhances the detection performance of YOLOv5s for colonic polyps.
  • The developed YOLOv5s+SEBiFPN model represents a promising advancement for computer-assisted diagnostic systems in colon cancer screening.
  • This improved AI approach has the potential to establish a benchmark for more effective early diagnosis of colon cancer.