Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus Images
Glaucoma is a leading cause of irreversible blindness, making early and accurate detection essential for effective management. This study investigates the use of deep learning for automated glaucoma diagnosis using fundus images from the JustRAIGS challenge dataset, which includes 101442 gradable i...
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| Format: | Article |
| Language: | English |
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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2025-05-01
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| Series: | Applied Medical Informatics |
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| Online Access: | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1185 |
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| author | Ruxandra-Mădălina FLORESCU Dragoş-Ovidiu ALEXANDRU Mircea-Sebastian ŞERBĂNESCU |
| author_facet | Ruxandra-Mădălina FLORESCU Dragoş-Ovidiu ALEXANDRU Mircea-Sebastian ŞERBĂNESCU |
| author_sort | Ruxandra-Mădălina FLORESCU |
| collection | DOAJ |
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Glaucoma is a leading cause of irreversible blindness, making early and accurate detection essential for effective management. This study investigates the use of deep learning for automated glaucoma diagnosis using fundus images from the JustRAIGS challenge dataset, which includes 101442 gradable images spanning both referable and non-referable glaucomatous cases. Three convolutional neural networks—ResNet18, GoogLeNet, and AlexNet—were evaluated for their ability to classify glaucomatous and non-glaucomatous eyes. The dataset was divided into 80% for training and validation and 20% for testing, with 10-fold cross-validation used for performance assessment. Among the models, AlexNet achieved the highest accuracy (91.27±3.14%) and AUC (0.95±0.02), outperforming ResNet18 and GoogLeNet. These findings underscore the potential of deep learning in automated glaucoma screening, offering a scalable and efficient diagnostic solution. Future work will integrate clinical input data and explore more advanced classification networks to further enhance diagnostic accuracy and robustness.
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| format | Article |
| id | doaj-art-a85528847a474a44927e3ff0fcbf5b6d |
| institution | DOAJ |
| issn | 2067-7855 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
| record_format | Article |
| series | Applied Medical Informatics |
| spelling | doaj-art-a85528847a474a44927e3ff0fcbf5b6d2025-08-20T02:39:51ZengIuliu Hatieganu University of Medicine and Pharmacy, Cluj-NapocaApplied Medical Informatics2067-78552025-05-0147Suppl. 1Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus ImagesRuxandra-Mădălina FLORESCU0Dragoş-Ovidiu ALEXANDRU1Mircea-Sebastian ŞERBĂNESCU2UMF CraiovaDepartment of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, Petru Rareş Str., no. 2-4, 200349 Craiova, Romania.Department of Medical Informatics and Biostatistics, University of Medicine and Pharmacy of Craiova, Petru Rareş Str., no. 2-4, 200349 Craiova, Romania Glaucoma is a leading cause of irreversible blindness, making early and accurate detection essential for effective management. This study investigates the use of deep learning for automated glaucoma diagnosis using fundus images from the JustRAIGS challenge dataset, which includes 101442 gradable images spanning both referable and non-referable glaucomatous cases. Three convolutional neural networks—ResNet18, GoogLeNet, and AlexNet—were evaluated for their ability to classify glaucomatous and non-glaucomatous eyes. The dataset was divided into 80% for training and validation and 20% for testing, with 10-fold cross-validation used for performance assessment. Among the models, AlexNet achieved the highest accuracy (91.27±3.14%) and AUC (0.95±0.02), outperforming ResNet18 and GoogLeNet. These findings underscore the potential of deep learning in automated glaucoma screening, offering a scalable and efficient diagnostic solution. Future work will integrate clinical input data and explore more advanced classification networks to further enhance diagnostic accuracy and robustness. https://ami.info.umfcluj.ro/index.php/AMI/article/view/1185Deep LearningTransfer LearningAutomated DiagnosisFundus Images |
| spellingShingle | Ruxandra-Mădălina FLORESCU Dragoş-Ovidiu ALEXANDRU Mircea-Sebastian ŞERBĂNESCU Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus Images Applied Medical Informatics Deep Learning Transfer Learning Automated Diagnosis Fundus Images |
| title | Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus Images |
| title_full | Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus Images |
| title_fullStr | Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus Images |
| title_full_unstemmed | Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus Images |
| title_short | Deep Learning with Transfer Learning for Automated Glaucoma Detection in Fundus Images |
| title_sort | deep learning with transfer learning for automated glaucoma detection in fundus images |
| topic | Deep Learning Transfer Learning Automated Diagnosis Fundus Images |
| url | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1185 |
| work_keys_str_mv | AT ruxandramadalinaflorescu deeplearningwithtransferlearningforautomatedglaucomadetectioninfundusimages AT dragosovidiualexandru deeplearningwithtransferlearningforautomatedglaucomadetectioninfundusimages AT mirceasebastianserbanescu deeplearningwithtransferlearningforautomatedglaucomadetectioninfundusimages |