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Detection and Classification of Gastrointestinal Diseases using Machine Learning.

Javeria Naz1, Muhammad Sharif1, Mussarat Yasmin1

  • 1Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.

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Summary

Computer-aided diagnosis (CAD) methods are crucial for analyzing gastrointestinal images, overcoming limitations of traditional endoscopy. This review details automated techniques for early GI disease detection, improving diagnostic accuracy.

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Computer Aided Design (CAD)Convolutional Neural Network (CNN)Gastrointestinal Tract (GIT)Wireless Capsule Endoscopy (WCE)handcrafted features.machine learning

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Area of Science:

  • Medical Imaging
  • Gastroenterology
  • Artificial Intelligence

Background:

  • Traditional endoscopy for gastrointestinal tract (GIT) examination is invasive and patient-unfriendly.
  • Video endoscopy (VE) and wireless capsule endoscopy (WCE) offer alternatives but generate vast image data.
  • Manual image analysis is time-consuming, necessitating automated Computer-Aided Diagnosis (CAD) systems.

Purpose of the Study:

  • To review and discuss existing state-of-the-art methods for automated classification, segmentation, and detection of gastrointestinal (GI) diseases.
  • To provide a comprehensive overview of techniques used in GI image analysis.
  • To identify challenges and limitations in current automated GI disease detection methods.

Main Methods:

  • Literature review categorizing methods by preprocessing, segmentation, handcrafted features, and deep learning.
  • Comparative analysis of various approaches for GI infection detection and classification.
  • Discussion of current issues, challenges, and limitations in the field.

Main Results:

  • A comparative analysis of different approaches for the detection and classification of GI infections was performed.
  • The review synthesizes information on various GI disease diagnosis methods.
  • Identified limitations and challenges in current automated systems.

Conclusions:

  • This review consolidates information on GI disease diagnosis methods, aiding researchers.
  • Facilitates the development of novel algorithms for earlier and more accurate GI disease detection.
  • Highlights the need for continued research to improve upon existing automated diagnostic approaches.