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

The Retina

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.

You might also read

Related Articles

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

Sort by

Same author

Genetic diversity and camphor profiling of <i>Curcuma caesia</i> roxb.: identification of elite genotypes and in silico prediction of gastroprotective mechanisms.

Frontiers in pharmacology·2026

Same author

Hybrid AI models for predicting heat distribution in complex tissue structures with bioheat transfer simulation.

Journal of thermal biology·2025

Same author

Ossicular Reconstruction in Chronic Otitis Media: A Systematic Review.

Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India·2025

Same author

Impact of cadmium on copepod Oithona similis at threshold concentrations: Determining safe exposure period.

Marine pollution bulletin·2025

Same author

Protecting digital assets using an ontology based cyber situational awareness system.

Frontiers in artificial intelligence·2025

Same author

Advancements in urban scene segmentation using deep learning and generative adversarial networks for accurate satellite image analysis.

PloS one·2024

Same journal

The role of digital resources in surgical education: An analysis of YouTube videos on dynamic stabilization.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026

Same journal

Behavioral patterns in iGaming across territories: Psychiatric and AI-driven insights via the internet of behavior.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026

Same journal

Leveraging personal health records for early heart failure risk prediction through AI-driven modeling.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026

Same journal

From data to prevention: A systematic review of artificial intelligence applications in sports injury prediction.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026

Same journal

Leadership styles and work outcome in healthcare sector: Insights from bibliometric analysis.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026

Same journal

Network analysis revealing research focus of the German Congress of Orthopedics and Trauma Surgery 2021.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 21, 2025

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Published on: January 12, 2024

Diabetic retinopathy detection via deep learning based dual features integrated classification model.

T M Devi¹, P Karthikeyan², B Muthu Kumar³

¹Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|March 19, 2025

Summary

This summary is machine-generated.

A novel deep learning framework, DD-FIC, accurately detects diabetic retinopathy (DR) from retinal images. This computer-vision approach enhances diagnostic speed and accuracy, aiding in preventing vision loss.

Keywords:

diabetic retinopathy deep learning global features local features random forest wavelet based retinex algorithm

More Related Videos

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: May 21, 2025

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Published on: January 12, 2024

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Ophthalmology
Computer Vision
Artificial Intelligence

Background:

Diabetic retinopathy (DR) detection is crucial for preventing blindness.
Manual diagnosis from retinal images is time-consuming and error-prone.
Intelligent systems offer a promising avenue for automated DR diagnosis.

Purpose of the Study:

To develop a novel deep learning framework for accurate DR detection.
To improve the efficiency and reliability of DR diagnosis.

Main Methods:

A Deep learning based Dual Features Integrated classification (DD-FIC) framework was designed.
Fundus images were denoised using the Wavelet integrated Retinex (WIR) algorithm.
Dual feature extraction (global and local) and Random Forest selection were employed, followed by multi-class support vector machine (MCSVM) classification.

Main Results:

The DD-FIC framework achieved a 98.6% detection accuracy on a Kaggle dataset.
Significant accuracy improvements were observed compared to existing methods.

Conclusions:

The proposed DD-FIC framework demonstrates high efficacy in detecting diabetic retinopathy.
This AI-driven approach offers a more efficient and accurate diagnostic tool.