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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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Diabetic Retinopathy Detection Using Local Extrema Quantized Haralick Features with Long Short-Term Memory Network.

Abubakar M Ashir¹, Salisu Ibrahim², Mohammed Abdulghani¹

¹Department of Computer Engineering, Tishk International University, Erbil, KRD, Iraq.

International Journal of Biomedical Imaging

|May 6, 2021

Summary

This study introduces a novel method for automatic diabetic retinopathy detection using fundus images. The approach enhances early diagnosis and treatment, potentially preventing blindness from diabetic eye disease.

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy is a leading cause of blindness.
Early detection and treatment are crucial for preventing vision loss.
Current diagnostic methods can be supplemented by automated techniques.

Purpose of the Study:

To propose a novel approach for automatic diabetic retinopathy detection.
To improve the accuracy and reduce false positives in diagnosing diabetic retinopathy symptoms.
To analyze retina vasculature and hard-exudate using fundus images.

Main Methods:

Feature extraction using local extrema information and quantized Haralick features.
Utilizing Long Short-Term Memory (LSTM) network with local extrema patterns for precise image analysis.
Employing a probabilistic approach to suppress false positives.

Main Results:

The proposed method demonstrated promising performance on two public datasets.
Evaluated using specificity, accuracy, and sensitivity, the approach showed significant indices.
Comparative analysis with state-of-the-art methods confirmed the validity and superiority of the proposed approach.

Conclusions:

The developed technique offers a robust and accurate method for diabetic retinopathy detection.
This automated approach can aid in earlier diagnosis and timely treatment, mitigating blindness risk.
The method's performance surpasses existing research, highlighting its clinical potential.