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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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Impact Factor : 8.14
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| Paper Title: | A Multi-Stage Transformer-U-Net and ELM Framework for Early Glaucoma Detection |
| Authors Name: | AYUSH KUMAR SAHU , RAKESH KUMAR KHARE |
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IJRTI_213611
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| Published Paper Id: | IJRTI2607004 |
| Published In: | Volume 11 Issue 7, July-2026 |
| DOI: | |
| Abstract: | Glaucoma is a progressive optic neuropathy and a leading cause of irreversible blind ness, where early detection is essential for preventing vision loss. However, existing automated approaches often suffer from limitations such as loss of spatial relation ships, lack of interpretability, and high computational complexity. To address these challenges, this paper proposes a novel multi-stage glaucoma detection framework that integrates Capsule Network (CapsNet)-based deep feature extraction with clinically relevant handcrafted features, unified through an Extreme Learning Machine (ELM) classifier. The preprocessing stage enhances retinal structures using red-channel extrac tion, contrast-limited adaptive histogram equalization (CLAHE), filtering, and normal ization. CapsNet effectively preserves spatial dependencies between optic disc and cup regions, while handcrafted features including GLCM texture, color statistics, entropy, and edge information incorporate domain-specific knowledge. The fused feature rep resentation is efficiently classified using ELM, enabling fast and accurate prediction. Additionally, SHAP-based explainability is employed to provide both global and local interpretability of model decisions. Experimental results on the ORIGA dataset demon strate that the proposed framework achieves superior performance, with an accuracy of 91.50%, sensitivity of 90.00%, specificity of 91.44%, and AUC of 0.9065, outperform ing conventional machine learning and deep learning models. These results highlight the effectiveness of combining deep and handcrafted features with an efficient classifier. The proposed approach offers a reliable, interpretable, and computationally efficient solution for automated glaucoma screening in clinical applications. |
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| Cite Article: | "A Multi-Stage Transformer-U-Net and ELM Framework for Early Glaucoma Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 7, page no.a29-a46, July-2026, Available :http://www.ijrti.org/papers/IJRTI2607004.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2607004
Registration ID:213611
Published In: Volume 11 Issue 7, July-2026
DOI (Digital Object Identifier):
Page No: a29-a46 Country: Raipur, Chhattisgarh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2607004 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2607004 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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