TY  - EJOU
AU  - Rahman, Atta 
AU  - Youldash, Mustafa 
AU  - Alshammari, Ghaida 
AU  - Sebiany, Abrar 
AU  - Alzayat, Joury 
AU  - Alsayed, Manar 
AU  - Alqahtani, Mona 
AU  - Aljishi, Noor 

TI  - Diabetic Retinopathy Detection: A Hybrid Intelligent Approach
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 80
IS  - 3
SN  - 1546-2226

AB  - Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy (DR). Early detection and treatment are crucial to prevent complete blindness or partial vision loss. Traditional detection methods, which involve ophthalmologists examining retinal fundus images, are subjective, expensive, and time-consuming. Therefore, this study employs artificial intelligence (AI) technology to perform faster and more accurate binary classifications and determine the presence of DR. In this regard, we employed three promising machine learning models namely, support vector machine (SVM), k-nearest neighbors (KNN), and Histogram Gradient Boosting (HGB), after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society (APTOS) (a standard dataset), which includes 3662 images and originally categorized DR into five levels, now simplified to a binary format: No DR and DR (Classes 1–4). The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset, achieving an excellent accuracy of 96.9%, compared to 95.6% for both the KNN and HGB models. This approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.
KW  - Diabetic retinopathy; transfer learning; machine learning; fundus images; binary classification; APTOS

DO  - 10.32604/cmc.2024.055106