Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images
Diabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proli...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-10-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2251231 |
| _version_ | 1827308991377571840 |
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| author | V. Selvakumar C. Akila |
| author_facet | V. Selvakumar C. Akila |
| author_sort | V. Selvakumar |
| collection | DOAJ |
| description | Diabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proliferative Diabetic Retinopathy), Moderate NPDR, Severe NPDR, and PDR. On retinal fundus images, an effective method for identifying diabetic retinopathy (DR) is proposed by combining the U-Net architecture with the K-nearest neighbours (KNN) algorithm. The U-Net architecture is used for segmenting exudates in retinal pictures, and the KNN algorithm is used for final classification. The combination of U-Net and KNN enables accurate feature extraction and efficient classification, effectively overcoming the computational challenges common to deep learning models. The experiments are carried out utilizing a publicly available dataset of retinal fundus images from Kaggle to assess the effectiveness of our suggested strategy. The proposed architecture provides precise output when compared to other models GoogleNet, ResNet18, and VGG16. The proposed model provides a training accuracy of 82.96% and detection of PDR with high accuracy in the short period which prevents loss of vision in early stage. |
| first_indexed | 2024-03-10T15:57:28Z |
| format | Article |
| id | doaj.art-02000308cecd4e57a68ca5485bc6a69e |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-04-24T19:21:27Z |
| publishDate | 2023-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-02000308cecd4e57a68ca5485bc6a69e2024-03-25T18:18:03ZengTaylor & Francis GroupAutomatika0005-11441848-33802023-10-016441148115710.1080/00051144.2023.2251231Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus imagesV. Selvakumar0C. Akila1AP / ECE Department, Government College of Engineering, Tirunelveli, IndiaAP / CSE, Anna University Regional Campus, Tirunelveli, IndiaDiabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proliferative Diabetic Retinopathy), Moderate NPDR, Severe NPDR, and PDR. On retinal fundus images, an effective method for identifying diabetic retinopathy (DR) is proposed by combining the U-Net architecture with the K-nearest neighbours (KNN) algorithm. The U-Net architecture is used for segmenting exudates in retinal pictures, and the KNN algorithm is used for final classification. The combination of U-Net and KNN enables accurate feature extraction and efficient classification, effectively overcoming the computational challenges common to deep learning models. The experiments are carried out utilizing a publicly available dataset of retinal fundus images from Kaggle to assess the effectiveness of our suggested strategy. The proposed architecture provides precise output when compared to other models GoogleNet, ResNet18, and VGG16. The proposed model provides a training accuracy of 82.96% and detection of PDR with high accuracy in the short period which prevents loss of vision in early stage.https://www.tandfonline.com/doi/10.1080/00051144.2023.2251231DRretinopathyU-netKNNdetection and classificationfundus image |
| spellingShingle | V. Selvakumar C. Akila Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images Automatika DR retinopathy U-net KNN detection and classification fundus image |
| title | Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images |
| title_full | Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images |
| title_fullStr | Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images |
| title_full_unstemmed | Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images |
| title_short | Efficient diabetic retinopathy diagnosis through U-Net – KNN integration in retinal fundus images |
| title_sort | efficient diabetic retinopathy diagnosis through u net knn integration in retinal fundus images |
| topic | DR retinopathy U-net KNN detection and classification fundus image |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2023.2251231 |
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