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 Experiment Video

Updated: Aug 25, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K

Artificial Humming Bird Optimization-Based Hybrid CNN-RNN for Accurate Exudate Classification from Fundus Images.

Dhiravidachelvi E1, Senthil Pandi S2, Prabavathi R3

  • 1Department of Electronics and Communication Engineering, Mohamed sathak engineering college, Kilakarai, Tamil Nadu, India. msajce.dhiravidachelvi@gmail.com.

Journal of Digital Imaging
|October 14, 2022
PubMed
Summary

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

Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance.

MethodsX·2025
Same author

Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification.

Electromagnetic biology and medicine·2024
See all related articles

This study introduces a hybrid deep learning model (HCNNRNN-AHB) for automated detection of exudates in fundus images, crucial for early diabetic retinopathy diagnosis. The novel approach achieves 97.4% accuracy, improving upon manual methods.

Area of Science:

  • Ophthalmology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computational Pathology

Background:

  • Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients.
  • Early detection of DR, specifically exudates in fundus images, is vital to prevent severe visual impairment.
  • Manual detection of exudates is time-consuming, labor-intensive, and prone to errors.

Purpose of the Study:

  • To develop an automated decision-making system for accurate and efficient detection of exudates in fundus images.
  • To enhance the early diagnosis of diabetic retinopathy by improving the classification of fundus images.

Main Methods:

  • A novel hybrid deep learning model, combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) with Artificial Hummingbird Optimization (AHB) (HCNNRNN-AHB), was developed.
Keywords:
Artificial hummingbird algorithm and exudatesConvolutional neural networkFundus imageRecurrent neural network

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Related Experiment Videos

Last Updated: Aug 25, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
  • Optic discs were removed using Hough transform to prevent false positives.
  • Color and texture features were extracted, followed by classification using the optimized HCNNRNN-AHB model.

    • Main Results:

    • The proposed HCNNRNN-AHB technique effectively classified fundus images into exudate and non-exudate categories.
    • The model achieved a high prediction and classification accuracy of 97.4%.
    • Performance was validated using metrics including accuracy, sensitivity, specificity, F-score, and AUC.

    Conclusions:

    • The automated HCNNRNN-AHB system offers a significant improvement over manual methods for exudate detection in diabetic retinopathy.
    • This AI-driven approach holds promise for enhancing the early and accurate diagnosis of diabetic retinopathy, potentially reducing vision impairment.