Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images
Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12722 |
| _version_ | 1797400470798467072 |
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| author | Silvia Alayón Jorge Hernández Francisco J. Fumero Jose F. Sigut Tinguaro Díaz-Alemán |
| author_facet | Silvia Alayón Jorge Hernández Francisco J. Fumero Jose F. Sigut Tinguaro Díaz-Alemán |
| author_sort | Silvia Alayón |
| collection | DOAJ |
| description | Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images. Among these intelligent systems, Convolutional Neural Networks (CNNs) stand out, although alternatives have recently appeared, such as Vision Transformers (ViTs) or hybrid systems, which are also highly efficient in image processing. The question that arises in the face of so many emerging methods is whether all these new techniques are really more efficient for the problem of glaucoma diagnosis than the CNNs that have been used so far. In this article, we present a comprehensive comparative study of all these DL models in glaucoma detection, with the aim of elucidating which strategies are significantly better. Our main conclusion is that there are no significant differences between the efficiency of both DL strategies for the medical diagnostic problem addressed. |
| first_indexed | 2024-03-09T01:55:03Z |
| format | Article |
| id | doaj.art-af897f63096446a991bc1707f0116c21 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:55:03Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-af897f63096446a991bc1707f0116c212023-12-08T15:11:31ZengMDPI AGApplied Sciences2076-34172023-11-0113231272210.3390/app132312722Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus ImagesSilvia Alayón0Jorge Hernández1Francisco J. Fumero2Jose F. Sigut3Tinguaro Díaz-Alemán4Department of Computer Science and Systems Engineering, University of La Laguna, 38200 Santa Cruz de Tenerife, SpainDepartment of Computer Science and Systems Engineering, University of La Laguna, 38200 Santa Cruz de Tenerife, SpainDepartment of Computer Science and Systems Engineering, University of La Laguna, 38200 Santa Cruz de Tenerife, SpainDepartment of Computer Science and Systems Engineering, University of La Laguna, 38200 Santa Cruz de Tenerife, SpainDepartment of Ophthalmology, Canary Islands University Hospital, 38320 Santa Cruz de Tenerife, SpainGlaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images. Among these intelligent systems, Convolutional Neural Networks (CNNs) stand out, although alternatives have recently appeared, such as Vision Transformers (ViTs) or hybrid systems, which are also highly efficient in image processing. The question that arises in the face of so many emerging methods is whether all these new techniques are really more efficient for the problem of glaucoma diagnosis than the CNNs that have been used so far. In this article, we present a comprehensive comparative study of all these DL models in glaucoma detection, with the aim of elucidating which strategies are significantly better. Our main conclusion is that there are no significant differences between the efficiency of both DL strategies for the medical diagnostic problem addressed.https://www.mdpi.com/2076-3417/13/23/12722convolutional neural networkvision transformer-based systemglaucomafundus imaging |
| spellingShingle | Silvia Alayón Jorge Hernández Francisco J. Fumero Jose F. Sigut Tinguaro Díaz-Alemán Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images Applied Sciences convolutional neural network vision transformer-based system glaucoma fundus imaging |
| title | Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images |
| title_full | Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images |
| title_fullStr | Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images |
| title_full_unstemmed | Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images |
| title_short | Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images |
| title_sort | comparison of the performance of convolutional neural networks and vision transformer based systems for automated glaucoma detection with eye fundus images |
| topic | convolutional neural network vision transformer-based system glaucoma fundus imaging |
| url | https://www.mdpi.com/2076-3417/13/23/12722 |
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