Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Enzyme-aware soil fertility prediction using dual optimization with improved SCSO.

Scientific reports·2026
Same author

MSSA: memory-driven and simplified scaled attention for enhanced image captioning.

Scientific reports·2026
Same author

CAN-DAQ: An open-source, cost-effective data capture device and software for automotive research.

HardwareX·2026
Same author

Multidrug-Resistant Extended-Spectrum Beta-Lactamase (ESBL) Producing Escherichia coli in Pet Birds of Bangladesh.

Veterinary medicine and science·2025
Same author

Designing of a multiepitope-based vaccine against echinococcosis utilizing the potent Ag5 antigen: Immunoinformatics and simulation approaches.

PloS one·2025
Same author

A wavelet and local binary pattern-based feature descriptor for the detection of chronic infection through thoracic X-ray images.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2024

Related Experiment Video

Updated: Sep 5, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Biomedical image retrieval using adaptive neuro-fuzzy optimized classifier system.

Janarthanan R1, Eshrag A Refaee2, Selvakumar K3

  • 1Centre for Artificial Intelligence, Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai 600069, India.

Mathematical Biosciences and Engineering : MBE
|July 8, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized image classification framework for medical imaging. The hybrid adaptive neuro-fuzzy approach enhances diagnostic image retrieval and analysis for medical research.

Keywords:
Hybrid adaptive neuro-fuzzy optimized classifier systembiomedical imagesimage retrieval systemimproved cuckoo search optimization

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

974

Related Experiment Videos

Last Updated: Sep 5, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

974

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Data Management

Background:

  • Increasing volume of medical images in patient care necessitates efficient retrieval systems.
  • Hospitals generate over 10 GB of imaging data daily per appliance.
  • Accurate identification of patient information from diagnostic images is crucial for research.

Purpose of the Study:

  • To propose an optimized classifier framework for medical image recovery.
  • To enhance the accuracy and reliability of diagnostic image analysis.
  • To address the challenges of vagueness in medical image datasets.

Main Methods:

  • Utilized a hybrid adaptive neuro-fuzzy approach for image classification.
  • Incorporated improved cuckoo search optimization for enhancing the classifier.
  • Employed fuzzy sets to represent data vagueness in similarity measurements.
  • Applied linear discriminant analysis (LDA) for determining score values.

Main Results:

  • The proposed hybrid adaptive neuro-fuzzy system demonstrated improved classification performance.
  • Preliminary findings suggest enhanced reliability and effectiveness in image estimation.
  • The method successfully handles vagueness in medical image data.

Conclusions:

  • The optimized classifier framework offers a reliable solution for medical image recovery.
  • This approach advances the potential for utilizing diagnostic images in medical research.
  • The study highlights the efficacy of hybrid adaptive neuro-fuzzy systems in medical data analysis.