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Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame

Fadiyah M Almutairi1, Sara A Althubiti2, Shabnam Mohamed Aslam3

  • 1Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.

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|July 10, 2025
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Summary
This summary is machine-generated.

A new deep learning tool accurately detects Autoimmune Gastritis (AIG) from endoscopic images with 99.33% accuracy. This AI advancement aids clinicians in early AIG diagnosis and intervention for better patient outcomes.

Keywords:
Autoimmune gastritisClassificationDeep-learningEndoscopy imagesEntropy thresholdingMoth-Flame algorithm

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Gastric Tract Disease (GTD) requires prompt diagnosis and intervention.
  • Autoimmune Gastritis (AIG) is a chronic GTD where early detection is crucial.
  • Endoscopic imaging is vital for GTD screening and severity assessment.

Purpose of the Study:

  • To develop a deep learning (DL) tool for detecting Autoimmune Gastritis (AIG) from endoscopic images.
  • To enhance the accuracy of AIG detection in clinical practice.
  • To provide clinicians with an AI-assisted tool for improved AIG diagnosis.

Main Methods:

  • Image collection, resizing, and pre-processing using Entropy-function and Moth-Flame (MF) Algorithm.
  • Deep-feature extraction using a DL model, followed by MF algorithm optimization and feature concatenation.
  • Classification using K-Nearest Neighbor and performance validation via five-fold cross-validation.

Main Results:

  • The developed DL tool achieved a high detection accuracy of 99.33% for AIG.
  • Performance was evaluated using individual, concatenated, and fused deep features.
  • The study demonstrated the efficacy of the DL model in analyzing clinical-grade endoscopic images.

Conclusions:

  • The DL tool offers a clinically significant advancement for AIG detection from endoscopic data.
  • The high accuracy validates the tool's potential to assist clinicians in diagnosing AIG.
  • This AI-driven approach can improve the management of Autoimmune Gastritis.