New methodology based on images processing for the diabetic retinopathy disease classification. (5th July 2022)

• Record Type: Journal Article
• Authors: Bensmail, Ilham; Messadi, Mahammed; Feroui, Amel; Lazouni, Amine; Bessaid, Abdelhafid
• Is Part Of: International journal of biomedical engineering and technology. Volume 39:Number 2(2022)
• Page Start: 170
• Page End: 187
• Publication Date: 2022-07-05
• Subjects: diabetic retinopathy -- microaneurysms -- machine learning -- haemorrhages -- classification -- K-nearest neighbour -- KNN -- support vector machine -- SVM -- multilayer perceptron -- MLP -- radial basic function -- RBF -- C45; Biomedical engineering -- Periodicals; 610.28
• Languages: English
• ISSNs: 1752-6418

Abstract

Diabetes is a chronic disease that cannot be cured, but can be treated and controlled. In the long run, a high blood sugar level causes complications, especially in the eyes, which leads to the development of diabetic retinopathy (DR). Poor care could cause blindness to the sick person. In this paper, we propose a new system for early detection of the DR. The tested algorithm includes several important phases, especially, the detection of the retinal lesions caused by the disease (microaneurysms and haemorrhages), through pretreatment and segmentation processes, as well as the classification of the different stages of non-proliferative DR. Several classifiers have been tested and the support vector machine (SVM) has given a very good sensitivity, specificity, and accuracy of 97.56%, 99.01%, 97.52%, respectively. These values show that our approach can be used for diagnostic assistance in ophthalmology.