Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database
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 p...
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doaj-d168ffcc0c8a4feea92d491255b452bc2021-03-30T15:06:36ZengIEEEIEEE Access2169-35362021-01-019235442355310.1109/ACCESS.2021.30561869343812Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading DatabaseAnas Bilal0https://orcid.org/0000-0002-7760-3374Guangmin Sun1Yu Li2Sarah Mazhar3Abdul Qadir Khan4Faculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaDiabetic 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.https://ieeexplore.ieee.org/document/9343812/Binary treesdiabetic retinopathyfeature extractionKNNmajority voting systemmulticlass classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anas Bilal Guangmin Sun Yu Li Sarah Mazhar Abdul Qadir Khan |
spellingShingle |
Anas Bilal Guangmin Sun Yu Li Sarah Mazhar Abdul Qadir Khan Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database IEEE Access Binary trees diabetic retinopathy feature extraction KNN majority voting system multiclass classification |
author_facet |
Anas Bilal Guangmin Sun Yu Li Sarah Mazhar Abdul Qadir Khan |
author_sort |
Anas Bilal |
title |
Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database |
title_short |
Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database |
title_full |
Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database |
title_fullStr |
Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database |
title_full_unstemmed |
Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database |
title_sort |
diabetic retinopathy detection and classification using mixed models for a disease grading database |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
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. |
topic |
Binary trees diabetic retinopathy feature extraction KNN majority voting system multiclass classification |
url |
https://ieeexplore.ieee.org/document/9343812/ |
work_keys_str_mv |
AT anasbilal diabeticretinopathydetectionandclassificationusingmixedmodelsforadiseasegradingdatabase AT guangminsun diabeticretinopathydetectionandclassificationusingmixedmodelsforadiseasegradingdatabase AT yuli diabeticretinopathydetectionandclassificationusingmixedmodelsforadiseasegradingdatabase AT sarahmazhar diabeticretinopathydetectionandclassificationusingmixedmodelsforadiseasegradingdatabase AT abdulqadirkhan diabeticretinopathydetectionandclassificationusingmixedmodelsforadiseasegradingdatabase |
_version_ |
1724180037020680192 |