Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine
Abstract Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent blindness. For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary c...
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Nature Portfolio
2024-11-01
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| Online Access: | https://doi.org/10.1038/s41598-024-79710-7 |
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| author | Debendra Muduli Rani Kumari Adnan Akhunzada Korhan Cengiz Santosh Kumar Sharma Rakesh Ranjan Kumar Dinesh Kumar Sah |
| author_facet | Debendra Muduli Rani Kumari Adnan Akhunzada Korhan Cengiz Santosh Kumar Sharma Rakesh Ranjan Kumar Dinesh Kumar Sah |
| author_sort | Debendra Muduli |
| collection | DOAJ |
| description | Abstract Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent blindness. For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary classification (glaucoma or healthy), allowing ophthalmologists to detect glaucoma disease correctly in less computational time. We proposed learning technique called fast discrete curvelet transform with wrapping (FDCT-WRP) to create feature set. This method is entitled extracting curve-like features and creating a feature set. The combined feature reduction techniques named as principal component analysis and linear discriminant analysis, have been applied to generate prominent features and decrease the feature vector dimension. Lastly, a newly improved learning algorithm encompasses a modified pelican optimization algorithm (MOD-POA) and an extreme learning machine (ELM) for classification tasks. In this MOD-POA+ELM algorithm, the modified pelican optimization algorithm (MOD-POA) has been utilized to optimize the parameters of ELM’s hidden neurons. The effectiveness has been evaluated using two standard datasets called G1020 and ORIGA with the $$10 \times 5$$ -fold stratified cross-validation technique to ensure reliable evaluation. Our employed scheme achieved the best results for both datasets obtaining accuracy of 93.25% (G1020 dataset) and 96.75% (ORIGA dataset), respectively. Furthermore, we have utilized seven Explainable AI methodologies: Vanilla Gradients (VG), Guided Backpropagation (GBP ), Integrated Gradients ( IG), Guided Integrated Gradients (GIG), SmoothGrad, Gradient-weighted Class Activation Mapping (GCAM), and Guided Grad-CAM (GGCAM) for interpretability examination, aiding in the advancement of dependable and credible automation of healthcare detection of glaucoma. |
| format | Article |
| id | doaj-art-ff48ef98c7b5414092be46f0a6134e3f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ff48ef98c7b5414092be46f0a6134e3f2025-08-20T02:08:19ZengNature PortfolioScientific Reports2045-23222024-11-0114111610.1038/s41598-024-79710-7Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machineDebendra Muduli0Rani Kumari1Adnan Akhunzada2Korhan Cengiz3Santosh Kumar Sharma4Rakesh Ranjan Kumar5Dinesh Kumar Sah6Department of Computer Science and Engineering, C.V. Raman Global UniversityDepartment of Computer Science, Birla Institute of TechnologyCollege of Computing and IT, Department of Data and Cybersecurity, University of Doha for Science and TechnologyDepartment of Electrical-Electronics Engineering, Istinye UniversityDepartment of Computer Science and Engineering, C.V. Raman Global UniversityDepartment of Computer Science and Engineering, C.V. Raman Global UniversityDepartment of Computer Science and Engineering, Indian Institute of TechnologyAbstract Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent blindness. For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary classification (glaucoma or healthy), allowing ophthalmologists to detect glaucoma disease correctly in less computational time. We proposed learning technique called fast discrete curvelet transform with wrapping (FDCT-WRP) to create feature set. This method is entitled extracting curve-like features and creating a feature set. The combined feature reduction techniques named as principal component analysis and linear discriminant analysis, have been applied to generate prominent features and decrease the feature vector dimension. Lastly, a newly improved learning algorithm encompasses a modified pelican optimization algorithm (MOD-POA) and an extreme learning machine (ELM) for classification tasks. In this MOD-POA+ELM algorithm, the modified pelican optimization algorithm (MOD-POA) has been utilized to optimize the parameters of ELM’s hidden neurons. The effectiveness has been evaluated using two standard datasets called G1020 and ORIGA with the $$10 \times 5$$ -fold stratified cross-validation technique to ensure reliable evaluation. Our employed scheme achieved the best results for both datasets obtaining accuracy of 93.25% (G1020 dataset) and 96.75% (ORIGA dataset), respectively. Furthermore, we have utilized seven Explainable AI methodologies: Vanilla Gradients (VG), Guided Backpropagation (GBP ), Integrated Gradients ( IG), Guided Integrated Gradients (GIG), SmoothGrad, Gradient-weighted Class Activation Mapping (GCAM), and Guided Grad-CAM (GGCAM) for interpretability examination, aiding in the advancement of dependable and credible automation of healthcare detection of glaucoma.https://doi.org/10.1038/s41598-024-79710-7ELMFDCT-WRPGlaucoma detectionIOPLDAMOD-POA |
| spellingShingle | Debendra Muduli Rani Kumari Adnan Akhunzada Korhan Cengiz Santosh Kumar Sharma Rakesh Ranjan Kumar Dinesh Kumar Sah Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine Scientific Reports ELM FDCT-WRP Glaucoma detection IOP LDA MOD-POA |
| title | Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine |
| title_full | Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine |
| title_fullStr | Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine |
| title_full_unstemmed | Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine |
| title_short | Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine |
| title_sort | retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine |
| topic | ELM FDCT-WRP Glaucoma detection IOP LDA MOD-POA |
| url | https://doi.org/10.1038/s41598-024-79710-7 |
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