An Efficient DenseNet for Diabetic Retinopathy Screening
This study aims to propose a novel deep learning framework, i.e., efficient DenseNet, for identifying diabetic retinopathy severity levels in retinal images. Diabetic retinopathy is an eye condition that damages blood vessels in the retina. Detecting diabetic retinopathy at the early stage can avoi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taiwan Association of Engineering and Technology Innovation
2023-04-01
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| Series: | International Journal of Engineering and Technology Innovation |
| Subjects: | |
| Online Access: | https://ojs.imeti.org/index.php/IJETI/article/view/10045 |
| _version_ | 1797808595132219392 |
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| author | Sheena Christabel Pravin Sindhu Priya Kanaga Sabapathy Suganthi Selvakumar Saranya Jayaraman Selvakumar Varadharajan Subramani |
| author_facet | Sheena Christabel Pravin Sindhu Priya Kanaga Sabapathy Suganthi Selvakumar Saranya Jayaraman Selvakumar Varadharajan Subramani |
| author_sort | Sheena Christabel Pravin |
| collection | DOAJ |
| description |
This study aims to propose a novel deep learning framework, i.e., efficient DenseNet, for identifying diabetic retinopathy severity levels in retinal images. Diabetic retinopathy is an eye condition that damages blood vessels in the retina. Detecting diabetic retinopathy at the early stage can avoid retinal detachment and effects leading to blindness in diabetic adults. A thin-layered efficient DenseNet model has been proposed with fewer training learnable parameters, leading to higher classification accuracy than the other deep learning models. The proposed deep learning framework for diabetic retinopathy severity level detection has an inbuilt automatic pre-processing module. Afterward, the efficient DenseNet model and classifier will provide data augmentation and higher-level feature extraction. The proposed efficient DenseNet framework is trained and tested using 13000 retinal fundus images within the diabetic retinopathy database and combined with the k-nearest neighbor classifier demonstrating the best classification accuracy of 98.40%.
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| first_indexed | 2024-03-13T06:39:58Z |
| format | Article |
| id | doaj.art-2dd359dd59b9469287b5f3ead797b4fc |
| institution | Directory Open Access Journal |
| issn | 2223-5329 2226-809X |
| language | English |
| last_indexed | 2024-03-13T06:39:58Z |
| publishDate | 2023-04-01 |
| publisher | Taiwan Association of Engineering and Technology Innovation |
| record_format | Article |
| series | International Journal of Engineering and Technology Innovation |
| spelling | doaj.art-2dd359dd59b9469287b5f3ead797b4fc2023-06-08T18:07:05ZengTaiwan Association of Engineering and Technology InnovationInternational Journal of Engineering and Technology Innovation2223-53292226-809X2023-04-0113210.46604/ijeti.2023.10045An Efficient DenseNet for Diabetic Retinopathy ScreeningSheena Christabel Pravin0Sindhu Priya Kanaga Sabapathy1Suganthi Selvakumar2Saranya Jayaraman3Selvakumar Varadharajan Subramani4School of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaDepartment of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, IndiaDepartment of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, IndiaDepartment of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, IndiaDepartment of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai, India This study aims to propose a novel deep learning framework, i.e., efficient DenseNet, for identifying diabetic retinopathy severity levels in retinal images. Diabetic retinopathy is an eye condition that damages blood vessels in the retina. Detecting diabetic retinopathy at the early stage can avoid retinal detachment and effects leading to blindness in diabetic adults. A thin-layered efficient DenseNet model has been proposed with fewer training learnable parameters, leading to higher classification accuracy than the other deep learning models. The proposed deep learning framework for diabetic retinopathy severity level detection has an inbuilt automatic pre-processing module. Afterward, the efficient DenseNet model and classifier will provide data augmentation and higher-level feature extraction. The proposed efficient DenseNet framework is trained and tested using 13000 retinal fundus images within the diabetic retinopathy database and combined with the k-nearest neighbor classifier demonstrating the best classification accuracy of 98.40%. https://ojs.imeti.org/index.php/IJETI/article/view/10045deep learningdiabetic retinopathyefficient DenseNetpre-processingclassification accuracy |
| spellingShingle | Sheena Christabel Pravin Sindhu Priya Kanaga Sabapathy Suganthi Selvakumar Saranya Jayaraman Selvakumar Varadharajan Subramani An Efficient DenseNet for Diabetic Retinopathy Screening International Journal of Engineering and Technology Innovation deep learning diabetic retinopathy efficient DenseNet pre-processing classification accuracy |
| title | An Efficient DenseNet for Diabetic Retinopathy Screening |
| title_full | An Efficient DenseNet for Diabetic Retinopathy Screening |
| title_fullStr | An Efficient DenseNet for Diabetic Retinopathy Screening |
| title_full_unstemmed | An Efficient DenseNet for Diabetic Retinopathy Screening |
| title_short | An Efficient DenseNet for Diabetic Retinopathy Screening |
| title_sort | efficient densenet for diabetic retinopathy screening |
| topic | deep learning diabetic retinopathy efficient DenseNet pre-processing classification accuracy |
| url | https://ojs.imeti.org/index.php/IJETI/article/view/10045 |
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