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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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Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms.

K K Mujeeb Rahman¹, Mohamed Nasor¹, Ahmed Imran¹

¹College of Engineering & Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates.

Diagnostics (Basel, Switzerland)

|September 23, 2022

Summary

Diabetic retinopathy, a diabetes complication causing vision loss, can be detected early using machine learning. This study developed AI models, achieving high accuracy in predicting diabetic retinopathy from fundus images.

Keywords:

DNN GLCM feature MATLAB SVM diabetic retinopathy fundus image image segmentation machine learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy is a leading cause of blindness, linked to diabetes.
Current diagnostic methods rely on manual analysis of fundus images, which is time-consuming and expensive.
Early detection is crucial to prevent vision loss and complications.

Purpose of the Study:

To develop an accessible machine learning tool for accurate diabetic retinopathy prediction.
To utilize digital fundus images for automated screening.

Main Methods:

Collected and annotated fundus images from public datasets.
Implemented and evaluated two machine learning models: Support Vector Machine (SVM) and Deep Neural Network (DNN).

Main Results:

The Support Vector Machine (SVM) model achieved a mean Area Under the ROC Curve (AUC) of 97.11%.
The Deep Neural Network (DNN) model demonstrated superior performance with a mean AUC of 99.15% on test data.

Conclusions:

Machine learning models, particularly DNNs, show significant potential for accurate and efficient diabetic retinopathy detection.
This approach can aid in early diagnosis, potentially reducing healthcare costs and improving patient outcomes.