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Diabetic Retinopathy

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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Related Experiment Video

Updated: Jul 8, 2026

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

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Published on: March 12, 2022

Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification.

Anas Bilal^1,2, Muhammad Shafiq³, Waeal J Obidallah⁴

¹College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.

Scientific Reports

|April 15, 2025

Summary

This summary is machine-generated.

Early detection of diabetic retinopathy (DR) is crucial to prevent blindness. This study introduces a hybrid Quantum Chimp Optimization Algorithm (QCOA) and SqueezeNet model for highly accurate DR classification, improving patient outcomes.

Keywords:

Chimp optimization Diabetic retinopathy Multi-class classification Quantum computing Support vector machine

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Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

Published on: January 24, 2025

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy (DR) is a leading cause of preventable blindness globally.
Prolonged hyperglycemia damages retinal blood vessels, necessitating early detection for intervention.
Current diagnostic methods require enhancement for improved accuracy and efficiency.

Purpose of the Study:

To develop and validate an advanced hybrid approach for enhanced diabetic retinopathy classification.
To improve the accuracy, sensitivity, and specificity of DR detection using artificial intelligence.
To facilitate earlier diagnosis and intervention for diabetic retinopathy to prevent vision loss.

Main Methods:

A hybrid model integrating Quantum Chimp Optimization Algorithm (QCOA) with SqueezeNet for feature extraction and classification.
SqueezeNet efficiently extracts critical features from segmented fundus images with low computational cost.
QCOA optimizes Support Vector Machine (SVM) parameters and performs feature selection for refined classification.

Main Results:

The hybrid QCOA-SqueezeNet-SVM model achieved exceptional classification accuracy of 99.80%.
The system demonstrated high sensitivity (99.90%) and perfect specificity (100%) in DR detection.
The approach significantly enhanced SVM performance, optimizing the classification model.

Conclusions:

The proposed hybrid approach offers a highly accurate and efficient method for diabetic retinopathy classification.
The integration of QCOA and SqueezeNet shows significant potential for improving early DR detection rates.
Clinical implementation of this AI-driven system can lead to better patient outcomes by enabling timely treatment.