Abstract :

Diabetic Retinopathy Is A Serious Eye Disease Caused By Diabetes And Is One Of The Leading Causes Of Blindness Worldwide. It Occurs When High Blood Sugar Levels Damage The Small Blood Vessels In The Retina, Leading To Leakage, Swelling, And Abnormal Blood Vessel Growth. If The Disease Is Not Detected And Treated In Its Early Stages, It May Lead To Severe Vision Loss Or Permanent Blindness. Therefore, Early Detection And Proper Diagnosis Of Diabetic Retinopathy Are Very Important To Prevent Complications And Improve The Quality Of Life For Diabetic Patients.Traditionally, Diabetic Retinopathy Is Diagnosed By Ophthalmologists Through Manual Examination Of Retinal Fundus Images. However, This Process Requires Experienced Medical Professionals And Can Be Time-consuming, Especially When Dealing With A Large Number Of Patients. With The Rapid Increase In The Number Of Diabetic Patients Worldwide, Manual Screening Alone Becomes Difficult And Inefficient. To Overcome These Challenges, Automated Systems Using Artificial Intelligence And Machine Learning Techniques Have Been Developed To Assist Doctors In The Early Detection And Classification Of Diabetic Retinopathy.The Main Objective Of This Project Is To Develop An Automated System For The Classification Of Diabetic Retinopathy Using Retinal Fundus Images. The System Uses Image Processing And Deep Learning Techniques To Analyze Retinal Images And Detect Abnormalities Associated With The Disease. The Process Involves Several Stages Including Image Preprocessing, Feature Extraction, And Classification.The Proposed System Aims To Assist Medical Professionals By Providing Faster And More Accurate Diagnosis Of Diabetic Retinopathy. It Reduces The Workload Of Doctors And Helps In Large-scale Screening Programs For Diabetic Patients. Automated Detection Systems Can Also Be Useful In Rural Or Remote Areas Where Access To Specialized Medical Professionals May Be Limited. By Integrating Artificial Intelligence With Healthcare Applications, The System Improves The Efficiency And Reliability Of Disease Detection.

Published:

10-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

323-331

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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