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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.

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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Using Retinal Imaging to Study Dementia

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RetinoDeep: Leveraging Deep Learning Models for Advanced Retinopathy Diagnostics.

Sachin Kansal¹, Bajrangi Kumar Mishra², Saniya Sethi³

¹Computer Science Engineering Department, Thapar Institute of Engineering Technology, Patiala 147004, Punjab, India.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

This study introduces RetinoDeep, a novel deep learning framework for automated diabetic retinopathy (DR) detection. The Bi-LSTM model optimized with Particle Swarm Optimization (PSO) demonstrated superior performance and interpretability for DR screening.

Keywords:

EfficientNetB0 SHAP explainability SPCL transformer ant colony optimization bidirectional LSTM data augmentation diabetic retinopathy particle swarm optimization

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Area of Science:

Ophthalmology
Computer Science
Artificial Intelligence

Background:

Diabetic retinopathy (DR) is a leading cause of vision loss globally.
Manual DR screening is labor-intensive, subjective, and faces specialist shortages.
There is a need for scalable, objective, and interpretable automated diagnostic tools for DR.

Purpose of the Study:

To develop and evaluate deep learning frameworks (RetinoDeep) for automated DR detection and classification across seven severity levels.
To enhance model transparency and clinical trustworthiness using explainable AI (XAI).
To improve the accuracy, stability, and generalization of DR screening systems.

Main Methods:

Proposed four novel deep learning models: EfficientNetB0 with SPCL transformer, ResNet50 with Bi-LSTM, GA-optimized Bi-LSTM, and SHAP-explained Bi-LSTM.
Trained and evaluated models on 757 augmented retinal fundus images.
Benchmarked against state-of-the-art models using accuracy, F1-score, and precision.

Main Results:

The Bi-LSTM model optimized with Particle Swarm Optimization (PSO) achieved superior stability and generalization.
SHAP visualizations confirmed that learned features align with key retinal biomarkers, enhancing interpretability.
The proposed models demonstrated improved diagnostic performance compared to baselines.

Conclusions:

RetinoDeep frameworks, particularly Bi-LSTM with PSO and SHAP, offer a promising approach for automated DR screening.
The integration of advanced optimization and explainable AI enhances diagnostic accuracy and clinical trustworthiness.
These systems have the potential for integration into real-world clinical workflows for improved DR management.