Digital illustration showing the transition from a healthy eye to a retina with detectable lesions.

Spotting Diabetes Early: A Guide to Understanding Diabetic Retinopathy

Soraya Malik in Health & Wellness June 2025 • 4 min read.

"Learn how new technology using LBP and SVM is helping detect diabetic retinopathy early, preserving sight and improving lives."

Diabetic retinopathy (DR) is the most recurrent cause of new cases of blindness among adults aged 20-74 years. It is a systemic disease which affects up to 80 percent of almost all persons who have had diabetes for 10 years or more. DR is considered as one of the major causes of blindness in almost all developed countries. But Diabetic Retinopathy is in-emblematic in its beginning stage; diabetic patients do not undertake any eye diagnosis, which leads to blindness.

Early and reliable diagnosis can significantly slow the progression of DR. Recent research focuses on developing advanced techniques for detecting retinal lesions, which are indicative of the disease, allowing for timely intervention and management.

These lesions include microaneurysms, hemorrhages, and hard exudates. Due to the swelling of very small capillary vessels in the retina micro aneurysm are caused. To diagnose the diabetic retinopathy ophthalmologists usually analyze these lesions. Hemorrhages are situated in the middle layer of the retina. Abnormal bleeding of the blood vessels in the retina is called retinal hemorrhage. Exudates are lipid residues of serous leakage from damaged capillaries. Hard exudates are shiny pale white or yellow sharp edged features.

Multi-Scale LBP and SVM Classification

Digital illustration showing the transition from a healthy eye to a retina with detectable lesions.

Researchers have introduced a method employing Multi-scale Local Binary Pattern (LBP) feature extraction and Support Vector Machine (SVM) classification to enhance the detection of DR. This technique begins with preprocessing the Region of Interest (ROI) to focus on the Optic Nerve Head (ONH).

The properties such as shape, color, size and convergence contributes to identify ONH in the retinal image. Based on a binary SVM classification technique the feature extracted images are classified either hemorrhages and exudates are present in lesions or not. Also, the resultant Hemorrhages and the Exudates undergo a Probabilistic multi-label Lesion classification, where the results indicate the presence of diabetic retinopathy.

Benefits of multi-scale LBP and SVM Classification:

Enhances early detection of lesions.
Provides detailed retinal image analysis.
Offers potential for broader application in rural health.

By improving early detection and management, this classification helps reduce the burden of vision loss associated with diabetes, promoting better health outcomes and quality of life for those affected.

Looking Ahead

The application of multi-scale LBP features represents a significant advancement in the early detection of diabetic retinopathy. By enhancing the precision and speed of lesion identification, this technique holds the potential to transform how DR is managed. As technology evolves, integrating such innovative approaches into routine clinical practice may significantly reduce the incidence of diabetes-related blindness, ensuring better outcomes for at-risk populations.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.2174/157340561101150423105120, Alternate LINK

Title: Multi-Scale Lbp And Svm Classification To Identify Diabetic Retinopathy In Lesions

Subject: Radiology, Nuclear Medicine and imaging

Journal: Current Medical Imaging Reviews

Publisher: Bentham Science Publishers Ltd.

Authors: A. Sirajudeen, M. Ezhilarasi

Published: 2015-04-23

Everything You Need To Know

What is diabetic retinopathy, and why is early detection so important?

Diabetic retinopathy is a complication of diabetes that affects the eyes. Specifically, it damages the blood vessels in the retina. If left unmanaged, diabetic retinopathy can lead to significant vision impairment or even blindness. The early stages of diabetic retinopathy often show no symptoms, which underscores the importance of regular eye exams for people with diabetes. Early detection and timely management are critical in slowing the progression of the disease and preserving vision.

How does the multi-scale LBP and SVM classification technique work to detect diabetic retinopathy?

The multi-scale LBP and SVM classification technique uses Multi-scale Local Binary Pattern (LBP) for feature extraction and Support Vector Machine (SVM) for classification to detect diabetic retinopathy. It involves preprocessing retinal images to focus on the Optic Nerve Head (ONH), extracting features, and then classifying the images to determine if lesions like hemorrhages and exudates are present. The method then uses probabilistic multi-label lesion classification to confirm the presence of diabetic retinopathy. This approach enhances early detection and enables detailed retinal image analysis.

What specific types of lesions in the retina are indicative of diabetic retinopathy, and what are their characteristics?

Lesions indicative of diabetic retinopathy include microaneurysms, hemorrhages, and hard exudates. Microaneurysms are caused by the swelling of small capillary vessels in the retina. Hemorrhages are abnormal bleeding of blood vessels in the retina, located in the middle layer. Hard exudates are lipid residues from serous leakage of damaged capillaries and appear as shiny, pale white or yellow features. Ophthalmologists look for these lesions during eye exams to diagnose diabetic retinopathy.

What are the advantages of using multi-scale LBP and SVM classification for detecting diabetic retinopathy?

The benefits of using multi-scale LBP and SVM classification include enhanced early detection of lesions associated with diabetic retinopathy, which allows for earlier intervention and management. It also offers a detailed retinal image analysis, improving the precision of diagnosis. Furthermore, this technique has the potential for broader application, particularly in rural health settings where access to specialized diagnostic equipment may be limited. Early detection and management reduce vision loss and improve the quality of life for affected individuals.

What does the future hold for the early detection of diabetic retinopathy with technologies like multi-scale LBP features?

The application of Multi-scale Local Binary Pattern (LBP) features signifies an advancement in detecting diabetic retinopathy early. By improving the precision and speed of identifying lesions, this method could change how diabetic retinopathy is managed. Integrating such approaches into routine clinical practice could significantly decrease diabetes-related blindness, promising better results for at-risk individuals. Continuous research and adoption of new technologies will be important in preventing vision loss from diabetes.