An Ensemble Learning Approach for Glaucoma Detection in Retinal Images
To stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. CNNs can extract distinctive characteristics directly from the fund...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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OICC Press
2022-12-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/4976 |
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| author | Marwah M. Mahdi Mohammed Abdulkreem Mohammed Haider Al-Chalibi Bashar S. Bashar Hayder Adnan Sadeq Talib Mohammed Jawad Abbas |
| author_facet | Marwah M. Mahdi Mohammed Abdulkreem Mohammed Haider Al-Chalibi Bashar S. Bashar Hayder Adnan Sadeq Talib Mohammed Jawad Abbas |
| author_sort | Marwah M. Mahdi |
| collection | DOAJ |
| description | To stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. CNNs can extract distinctive characteristics directly from the fundus pictures, as opposed to the current automatic screening techniques. In this study, a CNN-based deep learning architecture is created for the categorization of normal and glaucomatous fundus pictures. In this paper, we propose a deep learning-based framework for the detection of glaucoma based on retinal images. Our proposed approach utilizes the two CNN-based models, namely Inception and DenseNet, in order to classify the input images. We also show the impact of transfer learning on the training and the validation processes and put forward an effective pipeline with lower trainable parameters for the target task. Our experiments on a collected dataset demonstrate the efficacy of the proposed model by achieving an accuracy of 93.84%, a precision of 92.83%, and a recall of 95.00%. |
| format | Article |
| id | doaj-art-b44ecb970c3e42a69321af0cd4324ebc |
| institution | OA Journals |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-b44ecb970c3e42a69321af0cd4324ebc2025-08-20T02:15:54ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962022-12-0116410.30486/mjee.2022.696522An Ensemble Learning Approach for Glaucoma Detection in Retinal ImagesMarwah M. Mahdi0Mohammed Abdulkreem Mohammed1Haider Al-Chalibi2Bashar S. Bashar3Hayder Adnan Sadeq4Talib Mohammed Jawad Abbas5Anesthesia Techniques Department, Al-Mustaqbal University College, Babylon, IraqDepartment of Anesthesia Techniques, Al-Noor University College, Bartella, IraqMedical Technical College, Al-Farahidi University, Baghdad, IraqDepartment of Medical instruments engineering techniques, Al-Farahidi University, Baghdad,10021, IraqAl-Hadi University College, Baghdad,10011, IraqMedical device engineering, Ashur University College, Baghdad, IraqTo stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. CNNs can extract distinctive characteristics directly from the fundus pictures, as opposed to the current automatic screening techniques. In this study, a CNN-based deep learning architecture is created for the categorization of normal and glaucomatous fundus pictures. In this paper, we propose a deep learning-based framework for the detection of glaucoma based on retinal images. Our proposed approach utilizes the two CNN-based models, namely Inception and DenseNet, in order to classify the input images. We also show the impact of transfer learning on the training and the validation processes and put forward an effective pipeline with lower trainable parameters for the target task. Our experiments on a collected dataset demonstrate the efficacy of the proposed model by achieving an accuracy of 93.84%, a precision of 92.83%, and a recall of 95.00%.https://oiccpress.com/mjee/article/view/4976Convolutional neural networksDenseNet. inceptionGlaucoma DetectionMedical Images AnalysisRetinal images |
| spellingShingle | Marwah M. Mahdi Mohammed Abdulkreem Mohammed Haider Al-Chalibi Bashar S. Bashar Hayder Adnan Sadeq Talib Mohammed Jawad Abbas An Ensemble Learning Approach for Glaucoma Detection in Retinal Images Majlesi Journal of Electrical Engineering Convolutional neural networks DenseNet. inception Glaucoma Detection Medical Images Analysis Retinal images |
| title | An Ensemble Learning Approach for Glaucoma Detection in Retinal Images |
| title_full | An Ensemble Learning Approach for Glaucoma Detection in Retinal Images |
| title_fullStr | An Ensemble Learning Approach for Glaucoma Detection in Retinal Images |
| title_full_unstemmed | An Ensemble Learning Approach for Glaucoma Detection in Retinal Images |
| title_short | An Ensemble Learning Approach for Glaucoma Detection in Retinal Images |
| title_sort | ensemble learning approach for glaucoma detection in retinal images |
| topic | Convolutional neural networks DenseNet. inception Glaucoma Detection Medical Images Analysis Retinal images |
| url | https://oiccpress.com/mjee/article/view/4976 |
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