Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Endoscopic Procedures III: Video Capsule Endoscopy

Endoscopic Procedures III: Video Capsule Endoscopy

Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Adaptive preprocessing and Cascaded Canny Edge Segmentation for cassava disease identification using HyperCapsInception-ResNet-V2-CNN.

Frontiers in plant science·2026

Same author

Eco-Friendly Approaches to Azo Dye Removal: The Role of Microbial Azo-Reductases.

Applied biochemistry and biotechnology·2025

Same author

Automatic Classification on Bio Medical Prognosisof Invasive Breast Cancer

Asian Pacific journal of cancer prevention : APJCP·2017

Same author

Automatic Colorectal Polyp Detection in Colonoscopy Video Frames

Asian Pacific journal of cancer prevention : APJCP·2016

Search research articles

Related Experiment Video

Updated: Feb 27, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Heuristic Classifier for Observe Accuracy of Cancer Polyp Using Video Capsule Endoscopy

Geetha K¹, Rajan C

¹Department of Information Technology, Excel Engineering College, India.

Asian Pacific Journal of Cancer Prevention : APJCP

|July 4, 2017

Summary

This study introduces a Genetic Fuzzy based Improved Kernel Support Vector Machine (GF-IKSVM) classifier for enhanced colon polyp detection using video capsule endoscopy. The novel approach achieves 94.4% accuracy, improving upon existing methods for diagnosing gastrointestinal conditions.

Keywords:

Polyps Colon cancer Video Capsule Endoscopy Colonoscopy Segmentation binary pattern

More Related Videos

Diagnosis of Neoplasia in Barrett’s Esophagus using Vital-dye Enhanced Fluorescence Imaging

Diagnosis of Neoplasia in Barrett’s Esophagus using Vital-dye Enhanced Fluorescence Imaging

Published on: May 11, 2014

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Related Experiment Videos

Last Updated: Feb 27, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Diagnosis of Neoplasia in Barrett’s Esophagus using Vital-dye Enhanced Fluorescence Imaging

Diagnosis of Neoplasia in Barrett’s Esophagus using Vital-dye Enhanced Fluorescence Imaging

Published on: May 11, 2014

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Area of Science:

Gastroenterology
Medical Imaging
Artificial Intelligence

Background:

Colonoscopy and video capsule endoscopy are crucial for detecting colorectal polyps and cancer.
Automatic polyp detection in colonoscopy images is challenging due to unique imaging characteristics.
Existing methods often focus on accuracy and speed, utilizing various data mining techniques.

Purpose of the Study:

To improve the accuracy and ease of polyp diagnosis in video capsule endoscopy.
To develop an automated system for detecting colon polyps, especially flat lesions.
To enhance the identification of polyp patterns using advanced image analysis.

Main Methods:

Image segmentation using pixel-level binary patterns, mid-pass filters, and neighbor gray levels.
A three-step process involving missing data imputation, dimensionality reduction, and classification.
Utilizing a dataset of 500 patients and employing a Genetic Fuzzy based Improved Kernel Support Vector Machine (GF-IKSVM) classifier.

Main Results:

The GF-IKSVM classifier achieved 94.4% accuracy in diagnosing polyps from video capsule endoscopy images.
Segmented images were predominantly round, refined through filtering, computer vision, and thresholding.
The proposed fuzzy system and genetic fuzzy approach outperformed existing literature methods.

Conclusions:

The GF-IKSVM classifier offers a robust and accurate solution for polyp disease diagnosis in video capsule endoscopy.
The developed method significantly enhances the accuracy of automated polyp detection.
This approach provides a valuable tool for relieving human analysis in gastrointestinal diagnostics.