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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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A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional

Inam Ullah Khan¹, Mohaimenul Azam Khan Raiaan², Kaniz Fatema¹

¹Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh.

|June 28, 2023

Summary

This summary is machine-generated.

An automated system using the DR-CCTNet model accurately classifies diabetic retinopathy (DR) severity. This AI approach offers a robust and efficient solution for early DR detection, aiding ophthalmologists and improving patient outcomes.

Keywords:

ablation study compact convolutional transformer diabetic retinopathy image pre-processing low pixel retinal fundus images

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Diabetic retinopathy (DR) is a leading cause of blindness globally in diabetic patients.
Current DR screening methods are labor-intensive and time-consuming, requiring expert ophthalmologists.
Early diagnosis and treatment are crucial for managing DR and preventing vision loss.

Purpose of the Study:

To develop an automated system for diabetic retinopathy severity classification.
To overcome the limitations of manual DR screening through an AI-powered solution.
To improve the efficiency and accuracy of DR detection.

Main Methods:

A novel DR-CCTNet model was developed, modifying the Compact Convolutional Transformer (CCT) for large datasets and efficiency.
Five datasets were combined into a large dataset of 53,185 images.
Image pre-processing and 12 augmentation techniques were applied to enhance data quality and quantity.

Main Results:

The DR-CCTNet model achieved a test accuracy of 90.17% in classifying diabetic retinopathy.
Performance was robust, showing high accuracy even with low-pixel and limited image sets.
The proposed model significantly outperformed established transfer learning models like VGG19, ResNet50, VGG16, and MobileNetV2.

Conclusions:

The DR-CCTNet model presents a novel, accurate, and efficient method for automated diabetic retinopathy classification.
This AI approach can potentially reduce the workload for ophthalmologists.
The system promises to expedite treatment for patients, improving overall DR management.