Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms
Diabetic retinopathy is one of the leading causes of vision impairment noticed among individuals with prolonged diabetes. Early-stage detection is very crucial for its treatment. Now, we present a hybrid model which is a combination of U-Net algorithm used for image segmentation and Vision Transf...
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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/2084/ http://eprints.intimal.edu.my/2084/2/113 http://eprints.intimal.edu.my/2084/3/ij2024_47r.pdf |
| _version_ | 1848766915632168960 |
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| author | Mudit, Khater |
| author_facet | Mudit, Khater |
| author_sort | Mudit, Khater |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Diabetic retinopathy is one of the leading causes of vision impairment noticed among
individuals with prolonged diabetes. Early-stage detection is very crucial for its treatment.
Now, we present a hybrid model which is a combination of U-Net algorithm used for image
segmentation and Vision Transformer for classification. The total integration offers a robust
model which helps in detecting various stages of diabetic retinopathy. We leverage the use of
U-Net algorithm in image segmentation process to delineate the regions of interest in retinal
images. Further, the outputs which are segmented are passed into Vision Transformer, which is
enhanced by Efficient Net, which is used across various severity levels involved in Diabetic
Retinopathy. The usage of transformer architecture helps improve feature extraction and
classification performance which ensures that our model captures all patterns in retinal images.
We have evaluated our model on APTOS Blindness detection dataset in which our model
outperforms traditional convolutional neural networks-based models. Hence, the hybrid
approach consisting of combination of both the algorithms demonstrates excellent robustness
and generalization which offers a promising application for diabetic retinopathy screening,
involving the potential to revolutionize early diagnosis in clinical settings. |
| first_indexed | 2025-11-14T11:58:44Z |
| format | Article |
| id | intimal-2084 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:44Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20842025-07-12T07:29:43Z http://eprints.intimal.edu.my/2084/ Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms Mudit, Khater QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine T Technology (General) Diabetic retinopathy is one of the leading causes of vision impairment noticed among individuals with prolonged diabetes. Early-stage detection is very crucial for its treatment. Now, we present a hybrid model which is a combination of U-Net algorithm used for image segmentation and Vision Transformer for classification. The total integration offers a robust model which helps in detecting various stages of diabetic retinopathy. We leverage the use of U-Net algorithm in image segmentation process to delineate the regions of interest in retinal images. Further, the outputs which are segmented are passed into Vision Transformer, which is enhanced by Efficient Net, which is used across various severity levels involved in Diabetic Retinopathy. The usage of transformer architecture helps improve feature extraction and classification performance which ensures that our model captures all patterns in retinal images. We have evaluated our model on APTOS Blindness detection dataset in which our model outperforms traditional convolutional neural networks-based models. Hence, the hybrid approach consisting of combination of both the algorithms demonstrates excellent robustness and generalization which offers a promising application for diabetic retinopathy screening, involving the potential to revolutionize early diagnosis in clinical settings. INTI International University 2024-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2084/2/113 text en cc_by_4 http://eprints.intimal.edu.my/2084/3/ij2024_47r.pdf Mudit, Khater (2024) Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms. INTI JOURNAL, 2024 (47). pp. 1-7. ISSN e2600-7320 https://intijournal.intimal.edu.my |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine T Technology (General) Mudit, Khater Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms |
| title | Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms |
| title_full | Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms |
| title_fullStr | Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms |
| title_full_unstemmed | Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms |
| title_short | Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms |
| title_sort | diabetic retinopathy detection model using hybrid of u-net and vision transformer algorithms |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine T Technology (General) |
| url | http://eprints.intimal.edu.my/2084/ http://eprints.intimal.edu.my/2084/ http://eprints.intimal.edu.my/2084/2/113 http://eprints.intimal.edu.my/2084/3/ij2024_47r.pdf |