Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection
Glaucoma is a leading cause of blindness worldwide and results from high eye pressure-induced damage to the optic nerves, thereby preventing visual information from reaching the brain. While glaucoma is incurable in its advanced stages, its early detection improves the treatment outcome. Recently, c...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/8053117 |
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| author | Brendan Ubochi Abayomi E. Olawumi John Macaulay Oyawoye I. Ayomide Kayode F. Akingbade |
| author_facet | Brendan Ubochi Abayomi E. Olawumi John Macaulay Oyawoye I. Ayomide Kayode F. Akingbade |
| author_sort | Brendan Ubochi |
| collection | DOAJ |
| description | Glaucoma is a leading cause of blindness worldwide and results from high eye pressure-induced damage to the optic nerves, thereby preventing visual information from reaching the brain. While glaucoma is incurable in its advanced stages, its early detection improves the treatment outcome. Recently, computational models for image classification have enabled early detection of glaucoma from OCT scans of patients and will potentially augment the medical diagnosis of this disease. Models that are based on the convolutional neural networks (CNNs) have shown promise in the early detection of glaucoma. These models vary in their architectures and their accuracies depend largely on the intrinsic nature of the training datasets used. Hence, in this work, a comparative analysis is performed on vanilla CNN, AlexNet, GoogLeNet, and ResNet50 using two popular glaucoma datasets (ACRIMA and ORIGA). With careful attention to exhaustive image processing, an impressive training accuracy of 89% and validation accuracy of 50% was obtained from the vanilla CNN, showing a high sensitivity of 88% in detecting glaucomatous patients from OCT scans. However, the other models (AlexNet, GoogLeNet, and ResNet5) overfitted with a large difference between the obtained training accuracy and validation accuracy. The results also reveal that ResNet50 has the highest computational cost compared to the rest of the models. The obtained results demonstrate the peculiarity of the dataset, its selectiveness of the most appropriate model, and the potential of deep neural networks (DNNs) as an effective screening tool for glaucoma, enabling prompt interventions, reducing healthcare costs, and helping optometrists make swift decisions. |
| format | Article |
| id | doaj-art-96c517c932b24d349ee9c3541c7d4de6 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-96c517c932b24d349ee9c3541c7d4de62025-02-02T23:14:32ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/8053117Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma DetectionBrendan Ubochi0Abayomi E. Olawumi1John Macaulay2Oyawoye I. Ayomide3Kayode F. Akingbade4Department of Electrical and Electronics EngineeringDepartment of MathematicsCardiff School of TechnologiesDepartment of Computer EngineeringDepartment of Information and Communication TechnologyGlaucoma is a leading cause of blindness worldwide and results from high eye pressure-induced damage to the optic nerves, thereby preventing visual information from reaching the brain. While glaucoma is incurable in its advanced stages, its early detection improves the treatment outcome. Recently, computational models for image classification have enabled early detection of glaucoma from OCT scans of patients and will potentially augment the medical diagnosis of this disease. Models that are based on the convolutional neural networks (CNNs) have shown promise in the early detection of glaucoma. These models vary in their architectures and their accuracies depend largely on the intrinsic nature of the training datasets used. Hence, in this work, a comparative analysis is performed on vanilla CNN, AlexNet, GoogLeNet, and ResNet50 using two popular glaucoma datasets (ACRIMA and ORIGA). With careful attention to exhaustive image processing, an impressive training accuracy of 89% and validation accuracy of 50% was obtained from the vanilla CNN, showing a high sensitivity of 88% in detecting glaucomatous patients from OCT scans. However, the other models (AlexNet, GoogLeNet, and ResNet5) overfitted with a large difference between the obtained training accuracy and validation accuracy. The results also reveal that ResNet50 has the highest computational cost compared to the rest of the models. The obtained results demonstrate the peculiarity of the dataset, its selectiveness of the most appropriate model, and the potential of deep neural networks (DNNs) as an effective screening tool for glaucoma, enabling prompt interventions, reducing healthcare costs, and helping optometrists make swift decisions.http://dx.doi.org/10.1155/2024/8053117 |
| spellingShingle | Brendan Ubochi Abayomi E. Olawumi John Macaulay Oyawoye I. Ayomide Kayode F. Akingbade Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection Journal of Electrical and Computer Engineering |
| title | Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection |
| title_full | Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection |
| title_fullStr | Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection |
| title_full_unstemmed | Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection |
| title_short | Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection |
| title_sort | comparative analysis of vanilla cnn and transfer learning models for glaucoma detection |
| url | http://dx.doi.org/10.1155/2024/8053117 |
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