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Related Concept Videos

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

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This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
113

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Related Experiment Video

Updated: Jul 25, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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A Comparative Analysis of Optimization Algorithms for Gastrointestinal Abnormalities Recognition and Classification

Javeria Naz1, Muhammad Imran Sharif1, Muhammad Irfan Sharif2

  • 1Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah 47040, Pakistan.

Biomedicines
|June 28, 2023
PubMed
Summary

This study introduces a novel hybrid method for diagnosing gastrointestinal disorders, significantly improving accuracy and aiding early treatment for reduced mortality. The advanced technique enhances image analysis for more effective disease detection.

Keywords:
deep learningfeatures fusionfeatures optimizationgastrointestinal tract diseasestomach cancerstomach diseaseswireless capsule endoscopy

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Gastroenterology

Background:

  • Gastrointestinal disorders are a major cause of mortality, with traditional diagnostic methods being time-consuming.
  • Accurate and early diagnosis is crucial for effective treatment and reducing mortality rates.

Purpose of the Study:

  • To propose a hybrid method for accurate diagnosis of gastrointestinal tract abnormalities.
  • To promote early treatment and reduce mortality associated with these disorders.

Main Methods:

  • The study employed dataset augmentation, preprocessing, and feature engineering (extraction, fusion, optimization).
  • Image enhancement used hybrid contrast stretching; deep features were extracted via transfer learning (ResNet18, XcepNet23) and ensembled with texture features.
  • Feature optimization utilized Binary Dragonfly Algorithm (BDA), Moth-Flame Optimization (MFO), and Particle Swarm Optimization (PSO).

Main Results:

  • The proposed method achieved superior accuracy on both the Hybrid (5 classes) and Kvasir-V1 (8 classes) datasets.
  • Quadratic Support Vector Machine (Q_SVM) achieved 100% accuracy on the Hybrid dataset and 99.24% on the Kvasir-V1 dataset.
  • The results demonstrated the effectiveness of the hybrid approach compared to recent methods.

Conclusions:

  • The developed hybrid method offers a promising, highly accurate solution for diagnosing gastrointestinal abnormalities.
  • This approach can significantly aid in early detection and treatment, potentially reducing mortality.
  • The integration of deep learning, texture features, and advanced optimization algorithms proved effective.