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

MRI Radiomics for Preoperative Microvascular Invasion Stratification in Hepatocellular Carcinoma: Comparative Analysis of Intratumoral, Peritumoral, and Combined Intratumoral-Peritumoral Approaches-A Systematic Review and Meta-analysis.

Journal of imaging informatics in medicine·2026
Same author

MultiRetNet: A Lightweight Explainable AI Approach to Diabetic Retinopathy Grading and DME Detection Using Fundus-OCT Fusion.

Journal of imaging·2026
Same author

Reconfiguring brain networks via lightweight dynamic connectivity framework: An EEG-based stress validation.

Computers in biology and medicine·2026
Same author

Artificial intelligence potential in ovarian endometriosis imaging: a comparative meta-analysis of transvaginal ultrasound-based AI models and human readers.

Abdominal radiology (New York)·2026
Same author

Uncertainty quantification-based DMEFNet for reliable modelling of heart sound signals.

Scientific reports·2026
Same author

Impact of Pollution on Mental Health: A Systematic Review of Associations, Methodological Challenges, and Future Directions.

Health science reports·2026

Related Experiment Video

Updated: Apr 8, 2026

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

22.5K

Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.

Sulaimon Ibrahim1, Pradeep Chowriappa1, Sumeet Dua2

  • 1Computer Science, Louisiana Tech University, Nethken Hall 121, 600 Dan Reneau Dr., #10348, Ruston, LA, 71272, USA.

Medical & Biological Engineering & Computing
|June 26, 2015
PubMed
Summary

A novel data-adaptive neuro-fuzzy inference system effectively detects early diabetes maculopathy symptoms. This machine learning approach achieved 98.55% accuracy, aiding physicians in preserving vision for diabetic retinopathy patients.

Keywords:
ClassificationDiabetic retinopathyDiagnosisFuzzy logicImage analysis

More Related Videos

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats
07:41

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats

Published on: October 23, 2020

7.1K
In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma
12:48

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

Published on: May 11, 2015

11.2K

Related Experiment Videos

Last Updated: Apr 8, 2026

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

22.5K
Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats
07:41

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats

Published on: October 23, 2020

7.1K
In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma
12:48

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma

Published on: May 11, 2015

11.2K

Area of Science:

  • Ophthalmology
  • Computer Science
  • Machine Learning

Background:

  • Prolonged diabetes retinopathy can lead to irreversible vision loss from diabetes maculopathy.
  • Early detection of diabetes maculopathy is crucial for timely medical intervention.
  • Existing classification methods for medical diagnosis have limitations in handling attribute data distribution.

Purpose of the Study:

  • To develop and evaluate a machine learning-based decision system for early detection of diabetes maculopathy.
  • To investigate the effectiveness of a data-adaptive neuro-fuzzy inference system in classifying diabetes maculopathy.
  • To improve upon traditional fuzzy logic partitioning methods for enhanced classification accuracy.

Main Methods:

  • A data-adaptive neuro-fuzzy inference system was developed, utilizing a data frequency-driven approach for attribute partitioning.
  • Membership functions were adapted based on attribute data distribution (fine vs. coarse).
  • The system generated classification rules tailored to attribute data characteristics.

Main Results:

  • The data-adaptive neuro-fuzzy inference system demonstrated superior performance compared to simpler partitioning methods.
  • The proposed method generated more effective classification rules.
  • An overall classification accuracy of 98.55% was achieved for detecting early diabetes maculopathy.

Conclusions:

  • The data-adaptive neuro-fuzzy inference system offers a highly accurate and effective approach for early diabetes maculopathy detection.
  • This machine learning model can serve as a valuable decision support tool for physicians.
  • The findings highlight the potential of adaptive fuzzy logic systems in medical diagnostics.