Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine
<p dir="ltr">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...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2024
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| _version_ | 1864513551202254848 |
|---|---|
| author | Debendra Muduli (20748758) |
| author2 | Rani Kumari (18590634) Adnan Akhunzada (20151648) Korhan Cengiz (19450537) Santosh Kumar Sharma (13769587) Rakesh Ranjan Kumar (17310796) Dinesh Kumar Sah (20748761) |
| author2_role | author author author author author author |
| author_facet | Debendra Muduli (20748758) Rani Kumari (18590634) Adnan Akhunzada (20151648) Korhan Cengiz (19450537) Santosh Kumar Sharma (13769587) Rakesh Ranjan Kumar (17310796) Dinesh Kumar Sah (20748761) |
| author_role | author |
| dc.creator.none.fl_str_mv | Debendra Muduli (20748758) Rani Kumari (18590634) Adnan Akhunzada (20151648) Korhan Cengiz (19450537) Santosh Kumar Sharma (13769587) Rakesh Ranjan Kumar (17310796) Dinesh Kumar Sah (20748761) |
| dc.date.none.fl_str_mv | 2024-11-29T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-024-79710-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Retinal_imaging_based_glaucoma_detection_using_modified_pelican_optimization_based_extreme_learning_machine/28441868 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Ophthalmology and optometry Engineering Biomedical engineering Information and computing sciences Artificial intelligence Machine learning ELM FDCT-WRP Glaucoma detection IOP LDA MOD-POA |
| dc.title.none.fl_str_mv | Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-024-79710-7" target="_blank">https://dx.doi.org/10.1038/s41598-024-79710-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e710f51b77215fe4a7afc111f0488877 |
| identifier_str_mv | 10.1038/s41598-024-79710-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28441868 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machineDebendra Muduli (20748758)Rani Kumari (18590634)Adnan Akhunzada (20151648)Korhan Cengiz (19450537)Santosh Kumar Sharma (13769587)Rakesh Ranjan Kumar (17310796)Dinesh Kumar Sah (20748761)Biomedical and clinical sciencesOphthalmology and optometryEngineeringBiomedical engineeringInformation and computing sciencesArtificial intelligenceMachine learningELMFDCT-WRPGlaucoma detectionIOPLDAMOD-POA<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-024-79710-7" target="_blank">https://dx.doi.org/10.1038/s41598-024-79710-7</a></p>2024-11-29T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-024-79710-7https://figshare.com/articles/journal_contribution/Retinal_imaging_based_glaucoma_detection_using_modified_pelican_optimization_based_extreme_learning_machine/28441868CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284418682024-11-29T09:00:00Z |
| spellingShingle | Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine Debendra Muduli (20748758) Biomedical and clinical sciences Ophthalmology and optometry Engineering Biomedical engineering Information and computing sciences Artificial intelligence Machine learning ELM FDCT-WRP Glaucoma detection IOP LDA MOD-POA |
| status_str | publishedVersion |
| 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 | Biomedical and clinical sciences Ophthalmology and optometry Engineering Biomedical engineering Information and computing sciences Artificial intelligence Machine learning ELM FDCT-WRP Glaucoma detection IOP LDA MOD-POA |