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An Integrated Architecture for Colorectal Polyp Segmentation: The µ-Net Framework with Explainable AI.

Mehedi Hasan Emon1, Proloy Kumar Mondal1, Md Ariful Islam Mozumder1,2

  • 1Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae-Si 50834, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces µ-Net, an AI tool that accurately detects and classifies polyps in colonoscopy images, improving early colorectal cancer (CRC) detection. Explainable AI enhances trust in its reliable performance for CRC screening.

Keywords:
colorectal cancerdeep learningexplainable artificial intelligencepolyp segmentationµ-Net

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Colorectal cancer (CRC) is a leading cause of cancer death globally.
  • Early detection via colonoscopy is crucial for reducing CRC mortality.
  • Manual polyp detection in colonoscopy is error-prone and inefficient.

Purpose of the Study:

  • To develop an automated, reliable deep learning method for polyp segmentation and classification in colonoscopy.
  • To enhance the accuracy and efficiency of colorectal cancer screening.
  • To improve early detection rates and patient outcomes through AI-assisted analysis.

Main Methods:

  • A novel deep learning architecture, µ-Net, was developed for polyp segmentation.
  • The Kvasir-SEG dataset was used for training and evaluation.
  • Explainable AI (XAI) techniques (saliency maps, Grad-CAM) were integrated for model interpretability.

Main Results:

  • µ-Net achieved a high Dice coefficient of 94.02%, surpassing existing segmentation models.
  • XAI techniques provided visual explanations, increasing confidence in the model's predictions.
  • The model demonstrated strong accuracy and potential for clinical application.

Conclusions:

  • The µ-Net framework offers a significant advancement in automated polyp screening.
  • It improves the precision and efficiency of colonoscopy image analysis.
  • This AI tool supports clinical decision-making for early CRC detection and prevention.