Machine Learning Based Prediction of Retinopathy Diseases Using Segmented Retinal Images

• Published in: Advances in distributed computing and artificial intelligence journal Vol. 14; p. e31737
• Main Authors: Saroj, Sushil Kumar, Kumar, Rakesh, Singh, Nagendra Pratap
• Format: Journal Article
• Language: English
• Published: Salamanca Ediciones Universidad de Salamanca 27.02.2025
• Subjects: Blood vessels; Classification; Color imagery; Diabetes; Diabetic retinopathy; Feature extraction; Glaucoma; Hypertension; Image segmentation; Machine learning; Medical imaging; Retinal images
• ISSN: 2255-2863; 2255-2863
• DOI: 10.14201/adcaij.31737

Diabetes, hypertension, obesity, glaucoma, etc. are severe and common retinopathy diseases today. Early age detection and diagnosis of these diseases can save human beings from many life threats. The retina’s blood vessels carry details of retinopathy diseases. Therefore, feature extraction from blood vessels is essential to classify these diseases. A segmented retinal image is only a vascular tree of blood vessels. Feature extraction is easy and efficient from segmented images. Today, there are existing different approaches in this field that use RGB images only to classify these diseases due to which their performance is relatively low. In the work, we have proposed a model based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma, and multi-class diseases. We have carried out extensive experiments on numerous images of DRIVE, HRF, STARE, and RIM-ONE DL datasets. The highest accuracy of the proposed approach is 90.90 %, 95.00 %, and 92.90 % for diabetic retinopathy, glaucoma, and multi-class diseases, respectively, which the model detected better than most of the methods in this field.