Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques
This study compares multiple image processing and deep learning methods to demonstrate an enhanced approach to glaucoma diagnosis. The approach focuses on noise reduction using median filtering and optic disc segmentation utilizing the U-Net and U-Net+ architectures. Capsule Networks were utilized f...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Photodiagnosis and Photodynamic Therapy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S157210002500153X |
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| author | B.P. Pradeep kumar Pramod K.B. Rangaiah Robin Augustine |
| author_facet | B.P. Pradeep kumar Pramod K.B. Rangaiah Robin Augustine |
| author_sort | B.P. Pradeep kumar |
| collection | DOAJ |
| description | This study compares multiple image processing and deep learning methods to demonstrate an enhanced approach to glaucoma diagnosis. The approach focuses on noise reduction using median filtering and optic disc segmentation utilizing the U-Net and U-Net+ architectures. Capsule Networks were utilized for feature extraction and Extreme Learning Machines (ELM) for diagnostic classification. Three datasets were evaluated, including DRISHTI-GS, DRIONS-DB, and HRF, utilizing important parameters such as accuracy, sensitivity, and specificity. The findings revealed that median filtering reduced noise by 97.88%, with a peak signal-to-noise ratio of 44.99. U-Net beat U-Net+ in optic disc in the process of segmentation with a Dice coefficient of 0.8557, a Jaccard index of 0.7307, and higher segmentation accuracy. The suggested model has great diagnostic accuracy, scoring 99% for DRISHTI-GS, 99.5% for DRIONS-DB, and 98.5% for HRF. These findings show that using deep learning approaches can increase glaucoma diagnosis accuracy and reliability, with important implications for healthcare applications and patient outcomes. |
| format | Article |
| id | doaj-art-449087a8ca7f4f23b69c374ae352ef0b |
| institution | DOAJ |
| issn | 1572-1000 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photodiagnosis and Photodynamic Therapy |
| spelling | doaj-art-449087a8ca7f4f23b69c374ae352ef0b2025-08-20T03:23:22ZengElsevierPhotodiagnosis and Photodynamic Therapy1572-10002025-08-015410462110.1016/j.pdpdt.2025.104621Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniquesB.P. Pradeep kumar0Pramod K.B. Rangaiah1Robin Augustine2Department of Computer Science and Design, Atria Institute of Technology, Bengaluru 560024, IndiaMicrowaves in Medical Engineering Group, Division of Solid State Electronics, Department of Electrical Engineering, Uppsala University, Box 65, SE-751 03, Uppsala, SwedenMicrowaves in Medical Engineering Group, Division of Solid State Electronics, Department of Electrical Engineering, Uppsala University, Box 65, SE-751 03, Uppsala, Sweden; Corresponding author.This study compares multiple image processing and deep learning methods to demonstrate an enhanced approach to glaucoma diagnosis. The approach focuses on noise reduction using median filtering and optic disc segmentation utilizing the U-Net and U-Net+ architectures. Capsule Networks were utilized for feature extraction and Extreme Learning Machines (ELM) for diagnostic classification. Three datasets were evaluated, including DRISHTI-GS, DRIONS-DB, and HRF, utilizing important parameters such as accuracy, sensitivity, and specificity. The findings revealed that median filtering reduced noise by 97.88%, with a peak signal-to-noise ratio of 44.99. U-Net beat U-Net+ in optic disc in the process of segmentation with a Dice coefficient of 0.8557, a Jaccard index of 0.7307, and higher segmentation accuracy. The suggested model has great diagnostic accuracy, scoring 99% for DRISHTI-GS, 99.5% for DRIONS-DB, and 98.5% for HRF. These findings show that using deep learning approaches can increase glaucoma diagnosis accuracy and reliability, with important implications for healthcare applications and patient outcomes.http://www.sciencedirect.com/science/article/pii/S157210002500153XGlaucomaRetinal intra-ocular regionFrame networksU-NET layersDRISHTI-GSDRIONS-DB |
| spellingShingle | B.P. Pradeep kumar Pramod K.B. Rangaiah Robin Augustine Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques Photodiagnosis and Photodynamic Therapy Glaucoma Retinal intra-ocular region Frame networks U-NET layers DRISHTI-GS DRIONS-DB |
| title | Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques |
| title_full | Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques |
| title_fullStr | Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques |
| title_full_unstemmed | Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques |
| title_short | Enhanced glaucoma detection using U-Net and U-Net+ architectures using deep learning techniques |
| title_sort | enhanced glaucoma detection using u net and u net architectures using deep learning techniques |
| topic | Glaucoma Retinal intra-ocular region Frame networks U-NET layers DRISHTI-GS DRIONS-DB |
| url | http://www.sciencedirect.com/science/article/pii/S157210002500153X |
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