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Classification of H. pylori Infection from Histopathological Images Using Deep Learning.

Abdullahi Umar Ibrahim^1,2, Fikret Dirilenoğlu³, Uğuray Payam Hacisalihoğlu⁴

¹Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia, Cyprus. Abdullahi.umaribrahim@neu.edu.tr.

Journal of Imaging Informatics in Medicine

|February 9, 2024

Summary

Deep learning models can accurately detect Helicobacter pylori (H. pylori) in histopathology images. ResNet101 achieved high accuracy, offering a faster alternative for diagnosing this common bacterial infection.

Keywords:

Helicobacter pylori K-fold cross-validation Deep learning Histopathological images Pre-trained models

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Area of Science:

Medical Diagnostics
Computational Pathology
Infectious Diseases

Background:

Helicobacter pylori (H. pylori) infection affects over 4 billion people globally, causing gastric diseases.
Current H. pylori diagnosis relies on labor-intensive histopathological examination of biopsies.
Existing methods are time-consuming and may miss low bacterial loads.

Purpose of the Study:

To evaluate the efficacy of five pre-trained deep learning models for H. pylori detection in histopathological images.
To compare the performance of EfficientNet-b0, DenseNet-201, ResNet-101, MobileNet-v2, and Xception.
To identify the most promising model for supporting H. pylori diagnosis.

Main Methods:

Binary classification of 204 histopathological images into H. pylori-positive and H. pylori-negative cases.
Utilized five distinct pre-trained convolutional neural network architectures.
Performed five-fold cross-validation to ensure robust performance assessment.

Main Results:

ResNet101 demonstrated superior performance among the evaluated models.
ResNet101 achieved an average accuracy of 0.920.
The model exhibited high sensitivity, specificity, PPV, NPV, F1 score, MCC, and Cohen's kappa.

Conclusions:

Deep learning models, particularly ResNet101, show significant potential for H. pylori diagnosis.
Effective H. pylori detection is achievable even with limited datasets.
ResNet101 can aid pathologists in precise and rapid H. pylori diagnosis.