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H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner.

Yasmin Mohd Yacob1,2, Hiam Alquran3,4, Wan Azani Mustafa2,5

  • 1Faculty of Electronic Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia.

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Summary

Early detection of atrophic gastritis (AG) caused by Helicobacter pylori (H. pylori) infection is vital. This study introduces an improved deep learning model achieving 98.2% accuracy for diagnosing AG, preventing progression to gastric cancer.

Keywords:
Canonical Correlation AnalysisH. pyloriReliefFShuffleNetatrophic gastritisconvolution neural networkdeep learningfeature fusiongeneralized additive model

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Atrophic gastritis (AG), often caused by *Helicobacter pylori* (*H. pylori*) infection, can progress to gastric cancer, a leading cause of cancer mortality.
  • Accurate and early detection of AG is critical for preventing severe outcomes.
  • Existing deep learning models for *H. pylori*-associated AG detection face challenges with increasing network depth and accuracy.

Purpose of the Study:

  • To develop an enhanced deep convolutional neural network (DCNN) model for accurate binary classification of normal versus atrophic gastritis in the gastric antrum.
  • To improve diagnostic accuracy and overcome limitations of existing DCNN architectures in detecting *H. pylori*-associated AG.
  • To integrate advanced feature extraction and selection techniques for robust disease identification.

Main Methods:

  • Utilized a DCNN incorporating pooling and channel shuffle for improved training of deeper networks.
  • Employed Canonical Correlation Analysis (CCA) for feature fusion from pre-trained ShuffleNet models.
  • Applied ReliefF feature selection and Generalized Additive Model (GAM) for final classification.

Main Results:

  • The proposed enhanced DCNN model achieved a testing accuracy of 98.2%.
  • The integration of CCA and ReliefF effectively fused and selected relevant features for classification.
  • The model demonstrated superior performance in distinguishing between normal and atrophic gastritis.

Conclusions:

  • The developed deep learning approach provides a highly accurate method for diagnosing *H. pylori*-associated atrophic gastritis.
  • This technique offers a promising upgrade to current diagnostic standards, potentially reducing risks associated with untreated AG.
  • The study highlights the potential of advanced AI techniques in early cancer detection and prevention.