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Research on improved intestinal image classification for LARS based on ResNet.

Peng Zan1, Hua Zhong1, Yutong Zhao1

  • 1Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200444, China.

The Review of Scientific Instruments
|December 31, 2022
PubMed
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A new diagnostic system and Res-SVDNet model improve intestinal disease diagnosis after low anterior resection surgery. This fusion method achieves 99.54% accuracy, outperforming other models for better patient care.

Area of Science:

  • Gastroenterology and Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Low anterior resection syndrome (LARS) is a common complication following rectal cancer surgery.
  • Current diagnostic methods for LARS and related intestinal dysfunction are limited.
  • There is a need for advanced diagnostic tools for intestinal diseases.

Purpose of the Study:

  • To design an integrated intestinal function diagnostic system.
  • To develop an efficient image processing and classification algorithm for intestinal wall analysis.
  • To validate a fusion method for diagnosing intestinal diseases in clinical settings.

Main Methods:

  • Development of a novel intestinal function diagnostic system.
  • Application of the Res-SVDNet neural network model for intestinal image classification.

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  • Comparative experimental analysis of different models on constructed datasets.

    • Main Results:

    • The Res-SVDNet model effectively addresses challenges of small sample sizes and network overfitting.
    • The proposed fusion method achieved a high diagnostic accuracy of 99.54%.
    • The system offers a comprehensive approach, overcoming limitations of single clinical monitoring methods.

    Conclusions:

    • The developed diagnostic system and Res-SVDNet model provide an efficient and accurate method for diagnosing intestinal diseases.
    • This approach enhances clinical monitoring and management of patients, particularly those with LARS.
    • The study validates the efficacy of AI-driven fusion diagnosis in gastroenterology.