Academic Journal
A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection.
| Τίτλος: | A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection. |
|---|---|
| Συγγραφείς: | Atia A; Department of Information Systems, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt. azzamohamed@fci.kfs.edu.eg., Abdel-Kader H; Department of Information Systems, Faculty of Computers and Information, Menoufia University, Shibin El-Kom, Al Minufiyah, Egypt., Abo-Seida OM; Department of Computer Science, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt., Abdelatey A; Department of Information Systems, Faculty of Computers and Information, Menoufia University, Shibin El-Kom, Al Minufiyah, Egypt.; Faculty of Computers and Artificial Intelligence, Menoufia National University, Tukh Tambisha, Al Minufiyah, Egypt. |
| Πηγή: | Scientific reports [Sci Rep] 2026 Apr 17; Vol. 16 (1). Date of Electronic Publication: 2026 Apr 17. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Ιατρικοί όροι (MeSH): | Glaucoma*/diagnosis , Glaucoma*/diagnostic imaging , Image Processing, Computer-Assisted*/methods , Image Interpretation, Computer-Assisted*/methods, Convolutional Neural Networks ; Humans ; Algorithms ; Deep Learning |
| Περίληψη: | A progressive neurological condition, glaucoma is one of the main causes of irreversible blindness in the globe. Early detection is crucial to preventing irreversible vision loss however conventional diagnostic methods are often time-consuming, and heavily reliant on clinical expertise. This study presents an innovative deep learning framework designed to automate glaucoma detection while addressing key challenges such as data imbalance, image quality inconsistencies, and the need for accurate feature extraction. The proposed framework begins with a dedicated preprocessing pipeline that enhances image clarity, and isolates clinically relevant regions of interest. To handle class imbalance, a novel hybrid data augmentation strategy is introduced, combining geometric transformations with adaptive Gaussian noise injection that adjusts intensity according to image characteristics, thereby simulating realistic clinical variability. At its core, the framework leverages a dual-stream CNN architecture that integrates DenseNet121 for structural feature extraction and ResNet50 for texture representation. A lightweight channel-wise attention mechanism is then introduced to selectively emphasize clinically significant channels while suppressing redundant features, thereby balancing efficiency and interpretability. To optimize model performance while reducing computational overhead, an Improved Manta Ray Foraging Optimization (IMRFO) algorithm is employed. IMRFO enhances the standard MRFO with Partial Centroid Opposition-Based Learning (PCOBL) to dynamically fine-tune hyperparameters, including augmentation settings and transfer learning configurations. Experimental validation was conducted on four public benchmark datasets, ACRIMA, Drishti-Gs, ORIGA, and RIM-ONE-DL, demonstrating the framework's superior performance across all evaluation metrics. The model achieved 100.00% accuracy, precision, recall, and AUC on both ACRIMA and Drishti-Gs, with losses of 0.003 and 0.001, respectively. On ORIGA, it reached 99.70% accuracy, 99.80% precision, 99.30% recall, and 99.50% AUC (loss = 0.01). On RIM-ONE-DL, the model scored 99.90% accuracy, 99.70% precision, 99.50% recall, and 99.70% AUC (loss = 0.006). These findings confirm the framework's robustness and clinical applicability for effective glaucoma screening. (© 2026. The Author(s).) |
| Competing Interests: | Declarations. Competing interests: The authors declare no competing interests. |
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| Contributed Indexing: | Keywords: Dual-stream convolutional neural network (CNN); Dynamic Gaussian noise; Glaucoma detection; Lightweight channel-wise attention mechanism |
| Entry Date(s): | Date Created: 20260417 Date Completed: 20260715 Latest Revision: 20260715 |
| Update Code: | 20260715 |
| PubMed Central ID: | PMC13090355 |
| DOI: | 10.1038/s41598-026-45384-6 |
| PMID: | 41998000 |
| Βάση Δεδομένων: | MEDLINE |
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| Header | DbId: cmedm DbLabel: MEDLINE An: 41998000 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Atia+A%22">Atia A</searchLink>; Department of Information Systems, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt. azzamohamed@fci.kfs.edu.eg.<br /><searchLink fieldCode="AU" term="%22Abdel-Kader+H%22">Abdel-Kader H</searchLink>; Department of Information Systems, Faculty of Computers and Information, Menoufia University, Shibin El-Kom, Al Minufiyah, Egypt.<br /><searchLink fieldCode="AU" term="%22Abo-Seida+OM%22">Abo-Seida OM</searchLink>; Department of Computer Science, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt.<br /><searchLink fieldCode="AU" term="%22Abdelatey+A%22">Abdelatey A</searchLink>; Department of Information Systems, Faculty of Computers and Information, Menoufia University, Shibin El-Kom, Al Minufiyah, Egypt.; Faculty of Computers and Artificial Intelligence, Menoufia National University, Tukh Tambisha, Al Minufiyah, Egypt. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101563288%22">Scientific reports</searchLink> [Sci Rep] 2026 Apr 17; Vol. 16 (1). <i>Date of Electronic Publication: </i>2026 Apr 17. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Nature+Publishing+Group%22">Nature Publishing Group </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101563288 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2045-2322 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220452322%22">20452322 </searchLink><i>NLM ISO Abbreviation: </i>Sci Rep <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: London : Nature Publishing Group, copyright 2011- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Glaucoma%22">Glaucoma*</searchLink>/<searchLink fieldCode="MM" term="%22Glaucoma+diagnosis%22">diagnosis</searchLink> <br /><searchLink fieldCode="MM" term="%22Glaucoma%22">Glaucoma*</searchLink>/<searchLink fieldCode="MM" term="%22Glaucoma+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Image+Interpretation%2C+Computer-Assisted%22">Image Interpretation, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Interpretation%2C+Computer-Assisted+methods%22">methods</searchLink><br /><searchLink fieldCode="MH" term="%22Convolutional+Neural+Networks%22">Convolutional Neural Networks</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Algorithms%22">Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Deep+Learning%22">Deep Learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A progressive neurological condition, glaucoma is one of the main causes of irreversible blindness in the globe. Early detection is crucial to preventing irreversible vision loss however conventional diagnostic methods are often time-consuming, and heavily reliant on clinical expertise. This study presents an innovative deep learning framework designed to automate glaucoma detection while addressing key challenges such as data imbalance, image quality inconsistencies, and the need for accurate feature extraction. The proposed framework begins with a dedicated preprocessing pipeline that enhances image clarity, and isolates clinically relevant regions of interest. To handle class imbalance, a novel hybrid data augmentation strategy is introduced, combining geometric transformations with adaptive Gaussian noise injection that adjusts intensity according to image characteristics, thereby simulating realistic clinical variability. At its core, the framework leverages a dual-stream CNN architecture that integrates DenseNet121 for structural feature extraction and ResNet50 for texture representation. A lightweight channel-wise attention mechanism is then introduced to selectively emphasize clinically significant channels while suppressing redundant features, thereby balancing efficiency and interpretability. To optimize model performance while reducing computational overhead, an Improved Manta Ray Foraging Optimization (IMRFO) algorithm is employed. IMRFO enhances the standard MRFO with Partial Centroid Opposition-Based Learning (PCOBL) to dynamically fine-tune hyperparameters, including augmentation settings and transfer learning configurations. Experimental validation was conducted on four public benchmark datasets, ACRIMA, Drishti-Gs, ORIGA, and RIM-ONE-DL, demonstrating the framework's superior performance across all evaluation metrics. The model achieved 100.00% accuracy, precision, recall, and AUC on both ACRIMA and Drishti-Gs, with losses of 0.003 and 0.001, respectively. On ORIGA, it reached 99.70% accuracy, 99.80% precision, 99.30% recall, and 99.50% AUC (loss = 0.01). On RIM-ONE-DL, the model scored 99.90% accuracy, 99.70% precision, 99.50% recall, and 99.70% AUC (loss = 0.006). These findings confirm the framework's robustness and clinical applicability for effective glaucoma screening.<br /> (© 2026. The Author(s).) – Name: Abstract Label: Competing Interests Group: Ab Data: Declarations. Competing interests: The authors declare no competing interests. – Name: Ref Label: References Group: RefInfo Data: Moreno, M. V., Houriet, C. & Grounauer, P. A. Ocular phantom-based feasibility study of an early diagnosis device for glaucoma. Sensors 21(2), 579 (2021). (PMID: <searchLink fieldCode="PM" term="%2233467457783066910%2E3390%2Fs21020579%22">33467457783066910.3390/s21020579)</searchLink><br />Tam, Y. C. et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis. Ophthalmology 121(11), 2081–2090 (2014). (PMID: <searchLink fieldCode="PM" term="%2210%2E1016%2Fj%2Eophtha%2E2014%2E05%2E013%22">10.1016/j.ophtha.2014.05.013)</searchLink><br />Kolář, R. Detection of glaucomatous eye via color fundus images using fractal dimensions. Radioengineering 17(3), 109–114 (2018).<br />Michelson, G., Warntges, S., Hornegger, J. & Lausen, B. The papilla as a screening parameter for early diagnosis of glaucoma. Dtsch. Arztebl. Int. 105(34–35), 583–589 (2018).<br />Pannu, R. et al. Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features. Comput. Med. Imaging Graph. https://doi.org/10.1016/j.compmedimag.2025.102559 (2025). (PMID: <searchLink fieldCode="PM" term="%2210%2E1016%2Fj%2Ecompmedimag%2E2025%2E10255940315660%22">10.1016/j.compmedimag.2025.10255940315660)</searchLink><br />Zubair, M., Yamin, A. & Khan, S. A. Automated detection of optic disc for the analysis of retina using color fundus image. 2013 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2013..<br />Kumar, B. N., Chauhan, R. P. & Dahiya, N. Detection of glaucoma using image processing techniques: A review. In Proceedings of the 2016 International Conference on Microelectronics, Computing, and Communications (MicroCom), Durgapur, India, pp. 1–9, 2016.<br />Qiu, K. et al. Application of the ISNT rules on retinal nerve fiber layer thickness and neuroretinal rim area in healthy myopic eyes. Acta Ophthalmol. 96(2), 161–167 (2018). (PMID: <searchLink fieldCode="PM" term="%222919715710%2E1111%2Faos%2E13586%22">2919715710.1111/aos.13586)</searchLink><br />Shoukat, A. & Akbar, S. Artificial intelligence techniques for glaucoma detection through retinal images. In Artificial Intelligence and Internet of Things, Springer, pp. 1–20, 2021.<br />Reguant, R., Brunak, S. & Saha, S. 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