Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database
| Autor: | Anas Bilal, Abdul Qadir Khan, Sarah Mazhar, Guangmin Sun, Yu Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science Feature extraction KNN 02 engineering and technology multiclass classification computer.software_genre 03 medical and health sciences 0302 clinical medicine Diabetes mellitus 0202 electrical engineering electronic engineering information engineering medicine General Materials Science Segmentation Grading (tumors) majority voting system Retina Database Blindness feature extraction General Engineering Image segmentation Diabetic retinopathy Binary trees medicine.disease Support vector machine diabetic retinopathy medicine.anatomical_structure 030221 ophthalmology & optometry 020201 artificial intelligence & image processing lcsh:Electrical engineering. Electronics. Nuclear engineering computer lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 9, Pp 23544-23553 (2021) |
| ISSN: | 2169-3536 |
| Popis: | Diabetic retinopathy (DR) is a primary cause of blindness in which damage occurs to the retina due to an accretion of sugar levels in the blood. Therefore, prior detection, classification, and diagnosis of DR can prevent vision loss in diabetic patients. We proposed a novel and hybrid approach for prior DR detection and classification. We combined distinctive models to make the DR detection process robust or less error-prone while determining the classification based on the majority voting method. The proposed work follows preprocessing feature extraction and classification steps. The preprocessing step enhances abnormality presence as well as segmentation; the extraction step acquires merely relevant features; and the classification step uses classifiers such as support vector machine (SVM), K-nearest neighbor (KNN), and binary trees (BT). To accomplish this work, multiple severities of disease grading databases were used and achieved an accuracy of 98.06%, sensitivity of 83.67%, and 100% specificity. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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