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Research on gastrointestinal polyp detection method based on improved YOLOv7.

Yiyan Zhang1, Baojie Zhang1,2, Ketao Ma1

  • 1School of Intelligent Manufacturing, Qingdao Huanghai University, Qingdao, China.

Frontiers in Oncology
|June 26, 2026
PubMed
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This study introduces an improved YOLOv7 model for detecting gastrointestinal polyps, enhancing accuracy by incorporating ECANet attention and EIoU loss. The enhanced model demonstrates superior performance in identifying polyps from endoscopic images.

Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Gastroenterology

Background:

  • Gastrointestinal polyp detection via endoscopy faces challenges from complex backgrounds and irrelevant factors, leading to reduced accuracy and missed diagnoses.
  • Accurate early detection of gastrointestinal lesions is crucial for effective patient outcomes.

Purpose of the Study:

  • To improve the accuracy and reliability of gastrointestinal polyp detection using an enhanced deep learning model.
  • To address the limitations of existing methods in handling complex endoscopic image data.

Main Methods:

  • An improved YOLOv7 model was developed for gastrointestinal polyp detection.
  • The ECANet attention mechanism was integrated into the YOLOv7 architecture to mitigate background interference.
Keywords:
ECANetEIoUYOLOv7deep learninggastrointestinal lesion detection

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  • The EIoU loss function replaced CIoU to refine bounding box predictions for polyps.

    • Main Results:

    • The enhanced YOLOv7 (EIoU + ECANet) model achieved a precision of 94%, recall of 88.7%, and mean average precision of 92.9% on the Kvasir-SEG dataset.
    • Significant improvements were observed in all evaluated metrics compared to the original YOLOv7 model.

    Conclusions:

    • The proposed YOLOv7 (EIoU + ECANet) model exhibits robust performance and strong generalization capabilities for gastrointestinal polyp detection.
    • This enhanced model offers a promising tool for improving the accuracy of early gastrointestinal lesion diagnosis.