{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T15:30:15Z","timestamp":1773847815711,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17081-3","type":"journal-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T06:01:33Z","timestamp":1697868093000},"page":"46087-46159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A novel hybridized feature selection strategy for the effective prediction of glaucoma in retinal fundus images"],"prefix":"10.1007","volume":"83","author":[{"given":"Law Kumar","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Munish","family":"Khanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shankar","family":"Thawkar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rekha","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"17081_CR1","doi-asserted-by":"crossref","first-page":"107629","DOI":"10.1016\/j.knosys.2021.107629","volume":"235","author":"M Alweshah","year":"2022","unstructured":"Alweshah M, Alkhalaileh S, Al-Betar MA, Bakar AA (2022) Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis. Knowledge-Based Syst 235:107629","journal-title":"Knowledge-Based Syst"},{"key":"17081_CR2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","volume":"13","author":"K Kourou","year":"2015","unstructured":"Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13:8\u201317","journal-title":"Comput Struct Biotechnol J"},{"issue":"2","key":"17081_CR3","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","volume":"36","author":"MF Akay","year":"2009","unstructured":"Akay MF (2009) Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 36(2):3240\u20133247","journal-title":"Expert Syst Appl"},{"key":"17081_CR4","doi-asserted-by":"crossref","unstructured":"Singh LK, Khanna M, Garg H, Singh R (2023) Emperor penguin optimization algorithm-and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images. Soft Comput:1-37","DOI":"10.1007\/s00500-023-08449-6"},{"key":"17081_CR5","unstructured":"Khanna M, Singh LK, Garg H (2023) A novel approach for human diseases prediction using nature inspired computing & machine learning approach. Multimed Tools Appl:1-37"},{"issue":"1","key":"17081_CR6","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s12652-017-0655-5","volume":"10","author":"M Prabukumar","year":"2019","unstructured":"Prabukumar M, Agilandeeswari L, Ganesan K (2019) An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier. J Ambient Intell Humanized Comput 10(1):267\u2013293","journal-title":"J Ambient Intell Humanized Comput"},{"issue":"1","key":"17081_CR7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s44196-021-00013-0","volume":"14","author":"C Mallika","year":"2021","unstructured":"Mallika C, Selvamuthukumaran S (2021) A hybrid crow search and grey wolf optimization technique for enhanced medical data classification in diabetes diagnosis system. Int J Comput Intell Syst 14(1):1\u201318","journal-title":"Int J Comput Intell Syst"},{"key":"17081_CR8","doi-asserted-by":"crossref","unstructured":"Wang LX, Jiang SY, Jiang SY (2021) A feature selection method via analysis of relevance, redundancy, and interaction. Expert Systems with Appl:115365","DOI":"10.1016\/j.eswa.2021.115365"},{"key":"17081_CR9","doi-asserted-by":"crossref","first-page":"115620","DOI":"10.1016\/j.eswa.2021.115620","volume":"185","author":"M Shafipour","year":"2021","unstructured":"Shafipour M, Rashno A, Fadaei S (2021) Particle distance rank feature selection by particle swarm optimization. Expert Syst Appl 185:115620","journal-title":"Expert Syst Appl"},{"key":"17081_CR10","doi-asserted-by":"crossref","first-page":"102137","DOI":"10.1016\/j.bspc.2020.102137","volume":"62","author":"N Thakur","year":"2020","unstructured":"Thakur N, Juneja M (2020) Classification of glaucoma using hybrid features with machine learning approaches. Biomed Signal Process Control 62:102137","journal-title":"Biomed Signal Process Control"},{"key":"17081_CR11","doi-asserted-by":"crossref","unstructured":"Singh LK, Pooja GH, Khanna M, Bhadoria RS (2021) An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus. Med Biol Eng Comput 59:333-353","DOI":"10.1007\/s11517-020-02307-5"},{"issue":"6","key":"17081_CR12","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s12530-022-09426-4","volume":"13","author":"LK Singh","year":"2022","unstructured":"Singh LK, Pooja GH, Khanna M (2022) Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets. Evolv Syst 13(6):807\u2013836","journal-title":"Evolv Syst"},{"key":"17081_CR13","doi-asserted-by":"crossref","first-page":"103468","DOI":"10.1016\/j.bspc.2021.103468","volume":"73","author":"LK Singh","year":"2022","unstructured":"Singh LK, Khanna M (2022) A novel multimodality based dual fusion integrated approach for efficient and early prediction of glaucoma. Biomed Signal Process Control 73:103468","journal-title":"Biomed Signal Process Control"},{"key":"17081_CR14","doi-asserted-by":"crossref","first-page":"108009","DOI":"10.1016\/j.compeleceng.2022.108009","volume":"101","author":"M Juneja","year":"2022","unstructured":"Juneja M, Thakur S, Uniyal A, Wani A, Thakur N, Jindal P (2022) Deep learning-based classification network for glaucoma in retinal images. Comput Electric Eng 101:108009","journal-title":"Comput Electric Eng"},{"issue":"19","key":"17081_CR15","doi-asserted-by":"crossref","first-page":"27737","DOI":"10.1007\/s11042-022-12826-y","volume":"81","author":"LK Singh","year":"2022","unstructured":"Singh LK, Pooja GH, Khanna M (2022) Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images. Multimed Tools Appl 81(19):27737\u201327781","journal-title":"Multimed Tools Appl"},{"key":"17081_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-019-01050-8","volume":"31","author":"M Juneja","year":"2020","unstructured":"Juneja M, Thakur N, Thakur S, Uniyal A, Wani A, Jindal P (2020) GC-NET for classification of glaucoma in the retinal fundus image. Machine Vis Appl 31:1\u201318","journal-title":"Machine Vis Appl"},{"key":"17081_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-019-01050-8","volume":"31","author":"M Juneja","year":"2020","unstructured":"Juneja M, Thakur S, Wani A, Uniyal A, Thakur N, Jindal P (2020) DC-Gnet for detection of glaucoma in retinal fundus imaging. Machine Vis Appl 31:1\u201314","journal-title":"Machine Vis Appl"},{"issue":"4-5","key":"17081_CR18","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","volume":"31","author":"K Doi","year":"2007","unstructured":"Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graphics 31(4-5):198\u2013211","journal-title":"Comput Med Imaging Graphics"},{"issue":"22","key":"17081_CR19","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, \u2026 Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus images. Jama 316(22):2402\u20132410","journal-title":"Jama"},{"issue":"7639","key":"17081_CR20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115\u2013118","journal-title":"Nature"},{"issue":"59","key":"17081_CR21","first-page":"101570","volume":"2020","author":"JI Orlando","year":"2019","unstructured":"Orlando JI, Fu H, Breda JB, van Keer K, Bathula DR, Diaz-Pinto A et al (2019) ORIGA Challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus images[J]. Med Image Anal 2020(59):101570","journal-title":"Med Image Anal"},{"issue":"1","key":"17081_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-018-0723-6","volume":"19","author":"MN Bajwa","year":"2019","unstructured":"Bajwa MN, Malik MI, Siddiqui SA, Dengel A, Shafait F, Neumeier W, Ahmed S (2019) Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med Inform Decision Making 19(1):1\u201316","journal-title":"BMC Med Inform Decision Making"},{"key":"17081_CR23","doi-asserted-by":"crossref","first-page":"77414","DOI":"10.1109\/ACCESS.2018.2882946","volume":"6","author":"F Guo","year":"2018","unstructured":"Guo F, Mai Y, Zhao X, Duan X, Fan Z, Zou B, Xie B (2018) Yanbao: a mobile app using the measurement of clinical parameters for glaucoma screening. IEEE Access 6:77414\u201377428","journal-title":"IEEE Access"},{"key":"17081_CR24","doi-asserted-by":"crossref","first-page":"103485","DOI":"10.1016\/j.compbiomed.2019.103485","volume":"115","author":"S Liu","year":"2019","unstructured":"Liu S, Hong J, Lu X, Jia X, Lin Z, Zhou Y, \u2026 Zhang H (2019) Joint optic disc and cup segmentation using semi-supervised conditional GANs. Comput Biol Med 115:103485","journal-title":"Comput Biol Med"},{"key":"17081_CR25","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/978-3-030-13969-8_6","volume-title":"Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics","author":"H Fu","year":"2019","unstructured":"Fu H, Cheng J, Xu Y, Liu J (2019) Glaucoma detection based on deep learning network in fundus image. In: deep learning and convolutional neural networks for medical imaging and clinical informatics. Springer, Cham, pp 119\u2013137"},{"issue":"14","key":"17081_CR26","doi-asserted-by":"crossref","first-page":"4916","DOI":"10.3390\/app10144916","volume":"10","author":"S Sreng","year":"2020","unstructured":"Sreng S, Maneerat N, Hamamoto K, Win KY (2020) Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images. Appl Sci 10(14):4916","journal-title":"Appl Sci"},{"issue":"10","key":"17081_CR27","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s11517-020-02237-2","volume":"58","author":"F Guo","year":"2020","unstructured":"Guo F, Li W, Tang J, Zou B, Fan Z (2020) Automated glaucoma screening method based on image segmentation and feature extraction. Med Biol Eng Comput 58(10):2567\u20132586","journal-title":"Med Biol Eng Comput"},{"issue":"1","key":"17081_CR28","doi-asserted-by":"crossref","first-page":"739","DOI":"10.32604\/cmc.2022.022457","volume":"72","author":"KA AlAfandy","year":"2022","unstructured":"AlAfandy KA, Omara H, El-Sayed HS, Baz M, Lazaar M, Faragallah OS, Al Achhab M (2022) Efficient classification of remote sensing images using two convolution channels and SVM. CMC-Comput Mater Continua 72(1):739\u2013753","journal-title":"CMC-Comput Mater Continua"},{"key":"17081_CR29","doi-asserted-by":"crossref","unstructured":"AlAfandy KA, Omara H, Lazaar M, Al Achhab M (2019) Artificial neural networks optimization and convolution neural networks to classifying images in remote sensing: a review. In Proceedings of the 4th International Conference on Big Data and Internet of Things (pp. 1-8)","DOI":"10.1145\/3372938.3372945"},{"issue":"5","key":"17081_CR30","doi-asserted-by":"crossref","first-page":"770","DOI":"10.25046\/aj050594","volume":"5","author":"KA AlAfandy","year":"2020","unstructured":"AlAfandy KA, Omara H, Lazaar M, Al Achhab M (2020) Using classic networks for classifying remote sensing images: comparative study. Adv Sci Technol Eng Syst J 5(5):770\u2013780","journal-title":"Adv Sci Technol Eng Syst J"},{"issue":"3","key":"17081_CR31","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TITB.2011.2119322","volume":"15","author":"UR Acharya","year":"2011","unstructured":"Acharya UR, Dua S, Du X, Chua CK (2011) Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans Inform Technol Biomed 15(3):449\u2013455","journal-title":"IEEE Trans Inform Technol Biomed"},{"issue":"1","key":"17081_CR32","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/TITB.2011.2176540","volume":"16","author":"S Dua","year":"2011","unstructured":"Dua S, Acharya UR, Chowriappa P, Sree SV (2011) Wavelet-based energy features for glaucomatous image classification. IEEE Trans Inform Technol Biomed 16(1):80\u201387","journal-title":"IEEE Trans Inform Technol Biomed"},{"key":"17081_CR33","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.knosys.2012.02.010","volume":"33","author":"MRK Mookiah","year":"2012","unstructured":"Mookiah MRK, Acharya UR, Lim CM, Petznick A, Suri JS (2012) Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowledge-Based Syst 33:73\u201382","journal-title":"Knowledge-Based Syst"},{"key":"17081_CR34","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.bspc.2013.11.006","volume":"10","author":"KP Noronha","year":"2014","unstructured":"Noronha KP, Acharya UR, Nayak KP, Martis RJ, Bhandary SV (2014) Automated classification of glaucoma stages using higher order cumulant features. Biomed Signal Process Control 10:174\u2013183","journal-title":"Biomed Signal Process Control"},{"key":"17081_CR35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.bspc.2014.09.004","volume":"15","author":"UR Acharya","year":"2015","unstructured":"Acharya UR, Ng EYK, Eugene LWJ, Noronha KP, Min LC, Nayak KP, Bhandary SV (2015) Decision support system for the glaucoma using Gabor transformation. Biomed Signal Process Control 15:18\u201326","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"17081_CR36","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.cmpb.2015.08.002","volume":"122","author":"A Issac","year":"2015","unstructured":"Issac A, Sarathi MP, Dutta MK (2015) An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput Methods Prog Biomed 122(2):229\u2013244","journal-title":"Comput Methods Prog Biomed"},{"issue":"1","key":"17081_CR37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40064-016-3175-4","volume":"5","author":"AA Salam","year":"2016","unstructured":"Salam AA, Khalil T, Akram MU, Jameel A, Basit I (2016) Automated detection of glaucoma using structural and non structural features. Springerplus 5(1):1\u201321","journal-title":"Springerplus"},{"issue":"6","key":"17081_CR38","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1007\/s10916-016-0482-9","volume":"40","author":"MS Haleem","year":"2016","unstructured":"Haleem MS, Han L, Van Hemert J, Fleming A, Pasquale LR, Silva PS, \u2026 Aiello LP (2016) Regional image features model for automatic classification between normal and glaucoma in fundus and scanning laser ophthalmoscopy (SLO) images. J Med Syst 40(6):132","journal-title":"J Med Syst"},{"issue":"2","key":"17081_CR39","first-page":"5","volume":"19","author":"M Claro","year":"2016","unstructured":"Claro M, Santos L, Silva W, Ara\u00fajo F, Moura N, Macedo A (2016) Automatic glaucoma detection based on optic disc segmentation and texture feature extraction. Clei Electronic J 19(2):5\u20135","journal-title":"Clei Electronic J"},{"key":"17081_CR40","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.cmpb.2015.10.010","volume":"124","author":"A Singh","year":"2016","unstructured":"Singh A, Dutta MK, ParthaSarathi M, Uher V, Burget R (2016) Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Prog Biomed 124:108\u2013120","journal-title":"Comput Methods Prog Biomed"},{"issue":"18","key":"17081_CR41","doi-asserted-by":"crossref","first-page":"19173","DOI":"10.1007\/s11042-017-4608-y","volume":"76","author":"JA de Sousa","year":"2017","unstructured":"de Sousa JA, de Paiva AC, de Almeida JDS, Silva AC, Junior GB, Gattass M (2017) Texture based on geostatistic for glaucoma diagnosis from fundus eye image. Multimed Tools Appl 76(18):19173\u201319190","journal-title":"Multimed Tools Appl"},{"key":"17081_CR42","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.compbiomed.2017.03.008","volume":"84","author":"JE Koh","year":"2017","unstructured":"Koh JE, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Sree SV, \u2026 Tong L (2017) Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies. Comput Biol Med 84:89\u201397","journal-title":"Comput Biol Med"},{"issue":"1","key":"17081_CR43","doi-asserted-by":"crossref","first-page":"53","DOI":"10.4258\/hir.2018.24.1.53","volume":"24","author":"A Septiarini","year":"2018","unstructured":"Septiarini A, Khairina DM, Kridalaksana AH, Hamdani H (2018) Automatic glaucoma detection method applying a statistical approach to fundus images. Healthcare Inform Res 24(1):53\u201360","journal-title":"Healthcare Inform Res"},{"issue":"2","key":"17081_CR44","doi-asserted-by":"crossref","first-page":"795","DOI":"10.13005\/bpj\/1434","volume":"11","author":"D Selvathi","year":"2018","unstructured":"Selvathi D, Prakash NB, Gomathi V, Hemalakshmi GR (2018) Fundus image classification using wavelet based features in detection of glaucoma. Biomed Pharmacol J 11(2):795\u2013805","journal-title":"Biomed Pharmacol J"},{"key":"17081_CR45","first-page":"82","volume":"8","author":"DC Shubhangi","year":"2019","unstructured":"Shubhangi DC, Parveen N (2019) A dynamic roi based Glaucoma detection and region estimation technique. Int J Comput Sci. Mobile Comput 8:82\u201386","journal-title":"Int J Comput Sci. Mobile Comput"},{"issue":"05","key":"17081_CR46","first-page":"1950039","volume":"31","author":"S Renukalatha","year":"2019","unstructured":"Renukalatha S, Suresh KV (2019) Classification of glaucoma using simplified-multiclass support vector machine. Biomed Eng: Appl Basis Commun 31(05):1950039","journal-title":"Biomed Eng: Appl Basis Commun"},{"issue":"3","key":"17081_CR47","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.media.2009.12.006","volume":"14","author":"R Bock","year":"2010","unstructured":"Bock R, Meier J, Ny\u00fal LG, Hornegger J, Michelson G (2010) Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal 14(3):471\u2013481","journal-title":"Med Image Anal"},{"issue":"1","key":"17081_CR48","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.bbe.2017.11.002","volume":"38","author":"U Raghavendra","year":"2018","unstructured":"Raghavendra U, Bhandary SV, Gudigar A, Acharya UR (2018) Novel expert system for glaucoma identification using non-parametric spatial envelope energy spectrum with fundus images. Biocybernet Biomed Eng 38(1):170\u2013180","journal-title":"Biocybernet Biomed Eng"},{"key":"17081_CR49","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.compbiomed.2017.06.017","volume":"88","author":"S Maheshwari","year":"2017","unstructured":"Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR (2017) Iterative variational mode decomposition based automated detection of glaucoma using fundus images. Comput Biol Med 88:142\u2013149","journal-title":"Comput Biol Med"},{"issue":"1","key":"17081_CR50","first-page":"159","volume":"97","author":"C Raja","year":"2013","unstructured":"Raja C, Gangatharan N (2013) Glaucoma detection in fundal retinal images using trispectrum and complex wavelet-based features. Eur J Sci Res 97(1):159\u2013171","journal-title":"Eur J Sci Res"},{"issue":"4","key":"17081_CR51","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s11633-014-0858-6","volume":"12","author":"C Raja","year":"2015","unstructured":"Raja C, Gangatharan N (2015) Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnoses. Int J Autom Comput 12(4):393\u2013401","journal-title":"Int J Autom Comput"},{"issue":"4","key":"17081_CR52","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.5370\/JEET.2015.10.4.1899","volume":"10","author":"C Raja","year":"2015","unstructured":"Raja C, Gangatharan N (2015) Optimal hyper analytic wavelet transform for glaucoma detection in fundal retinal images. J Electric Eng Technol 10(4):1899\u20131909","journal-title":"J Electric Eng Technol"},{"issue":"3","key":"17081_CR53","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1109\/JBHI.2016.2544961","volume":"21","author":"S Maheshwari","year":"2016","unstructured":"Maheshwari S, Pachori RB, Acharya UR (2016) Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images. IEEE J Biomed Health inform 21(3):803\u2013813","journal-title":"IEEE J Biomed Health inform"},{"issue":"2","key":"17081_CR54","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.bbe.2018.02.003","volume":"38","author":"TR Kausu","year":"2018","unstructured":"Kausu TR, Gopi VP, Wahid KA, Doma W, Niwas SI (2018) Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images. Biocybernet Biomed Eng 38(2):329\u2013341","journal-title":"Biocybernet Biomed Eng"},{"issue":"01","key":"17081_CR55","doi-asserted-by":"crossref","first-page":"1940011","DOI":"10.1142\/S0219519419400116","volume":"19","author":"R Sharma","year":"2019","unstructured":"Sharma R, Sircar P, Pachori RB, Bhandary SV, Acharya UR (2019) Automated glaucoma detection using center slice of higher order statistics. J Mech Med Biol 19(01):1940011","journal-title":"J Mech Med Biol"},{"key":"17081_CR56","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.compbiomed.2018.11.028","volume":"105","author":"S Maheshwari","year":"2019","unstructured":"Maheshwari S, Kanhangad V, Pachori RB, Bhandary SV, Acharya UR (2019) Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques. Comput Biol Med 105:72\u201380","journal-title":"Comput Biol Med"},{"issue":"13","key":"17081_CR57","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1049\/iet-ipr.2019.0036","volume":"13","author":"DK Agrawal","year":"2019","unstructured":"Agrawal DK, Kirar BS, Pachori RB (2019) Automated glaucoma detection using quasi-bivariate variational mode decomposition from fundus images. IET Image Process 13(13):2401\u20132408","journal-title":"IET Image Process"},{"issue":"15-16","key":"17081_CR58","doi-asserted-by":"crossref","first-page":"10341","DOI":"10.1007\/s11042-019-7224-1","volume":"79","author":"GG Jerith","year":"2020","unstructured":"Jerith GG, Kumar PN (2020) Recognition of Glaucoma by means of gray wolf optimized neural network. Multimed Tools Appl 79(15-16):10341\u201310361","journal-title":"Multimed Tools Appl"},{"key":"17081_CR59","doi-asserted-by":"crossref","first-page":"112971","DOI":"10.1016\/j.eswa.2019.112971","volume":"142","author":"A Zareie","year":"2020","unstructured":"Zareie A, Sheikhahmadi A, Jalili M (2020) Identification of influential users in social network using gray wolf optimization algorithm. Expert Syst Appl 142:112971","journal-title":"Expert Syst Appl"},{"key":"17081_CR60","doi-asserted-by":"crossref","first-page":"103252","DOI":"10.1016\/j.advengsoft.2022.103252","volume":"173","author":"B Arasteh","year":"2022","unstructured":"Arasteh B, Abdi M, Bouyer A (2022) Program source code comprehension by module clustering using combination of discretized gray wolf and genetic algorithms. Adv Eng Software 173:103252","journal-title":"Adv Eng Software"},{"key":"17081_CR61","doi-asserted-by":"crossref","first-page":"106651","DOI":"10.1016\/j.asoc.2020.106651","volume":"96","author":"R Purushothaman","year":"2020","unstructured":"Purushothaman R, Rajagopalan SP, Dhandapani G (2020) Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering. Appl Soft Comput 96:106651","journal-title":"Appl Soft Comput"},{"key":"17081_CR62","doi-asserted-by":"crossref","first-page":"105858","DOI":"10.1016\/j.compbiomed.2022.105858","volume":"148","author":"MH Nadimi-Shahraki","year":"2022","unstructured":"Nadimi-Shahraki MH, Zamani H, Mirjalili S (2022) Enhanced whale optimization algorithm for medical feature selection: a COVID-19 case study. Comput Biol Med 148:105858","journal-title":"Comput Biol Med"},{"key":"17081_CR63","doi-asserted-by":"crossref","first-page":"117012","DOI":"10.1016\/j.eswa.2022.117012","volume":"200","author":"M Ghobaei-Arani","year":"2022","unstructured":"Ghobaei-Arani M, Shahidinejad A (2022) A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment. Expert Syst Appl 200:117012","journal-title":"Expert Syst Appl"},{"key":"17081_CR64","doi-asserted-by":"crossref","first-page":"108361","DOI":"10.1016\/j.cie.2022.108361","volume":"171","author":"X Lin","year":"2022","unstructured":"Lin X, Yu X, Li W (2022) A heuristic whale optimization algorithm with niching strategy for global multi-dimensional engineering optimization. Comput Indust Eng 171:108361","journal-title":"Comput Indust Eng"},{"issue":"4","key":"17081_CR65","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1007\/s11440-022-01450-7","volume":"17","author":"J Zhou","year":"2022","unstructured":"Zhou J, Zhu S, Qiu Y, Armaghani DJ, Zhou A, Yong W (2022) Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm. Acta Geotechnica 17(4):1343\u20131366","journal-title":"Acta Geotechnica"},{"key":"17081_CR66","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s00521-017-3272-5","volume":"30","author":"H Faris","year":"2018","unstructured":"Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural comput Appl 30:413\u2013435","journal-title":"Neural comput Appl"},{"key":"17081_CR67","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Software 69:46\u201361","journal-title":"Adv Eng Software"},{"key":"17081_CR68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2019.03.004","volume":"48","author":"FS Gharehchopogh","year":"2019","unstructured":"Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evol Comput 48:1\u201324","journal-title":"Swarm Evol Comput"},{"key":"17081_CR69","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Software 95:51\u201367","journal-title":"Adv Eng Software"},{"key":"17081_CR70","doi-asserted-by":"crossref","first-page":"105341","DOI":"10.1016\/j.cmpb.2020.105341","volume":"192","author":"J Martins","year":"2020","unstructured":"Martins J, Cardoso JS, Soares F (2020) Offline computer-aided diagnosis for glaucoma detection using fundus images targeted at mobile devices. Comput Methods Prog Biomed 192:105341","journal-title":"Comput Methods Prog Biomed"},{"key":"17081_CR71","unstructured":"Abad PF, Coronado-Gutierrez D, Lopez C, Burgos-Artizzu XP (2021) Glaucoma patient screening from online retinal fundus images via Artificial Intelligence. medRxiv"},{"issue":"01","key":"17081_CR72","doi-asserted-by":"crossref","first-page":"2350012","DOI":"10.1142\/S0219467823500122","volume":"23","author":"A Elmoufidi","year":"2023","unstructured":"Elmoufidi A, Skouta A, Jai-Andaloussi S, Ouchetto O (2023) CNN with multiple inputs for automatic glaucoma assessment using fundus images. Int J Image Graphics 23(01):2350012","journal-title":"Int J Image Graphics"},{"issue":"2","key":"17081_CR73","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1002\/ima.22494","volume":"31","author":"P Elangovan","year":"2021","unstructured":"Elangovan P, Nath MK (2021) Glaucoma assessment from color fundus images using convolutional neural network. Int J Imaging Syst Technol 31(2):955\u2013971","journal-title":"Int J Imaging Syst Technol"},{"key":"17081_CR74","doi-asserted-by":"crossref","unstructured":"Tulsani A, Kumar P, Pathan S (2021) Automated segmentation of optic disc and optic cup for glaucoma assessment using improved UNET++ architecture. Biocybernet Biomed Eng","DOI":"10.1016\/j.bbe.2021.05.011"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17081-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17081-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17081-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T11:42:36Z","timestamp":1714390956000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17081-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":74,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17081"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17081-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"16 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Data will be made available on reasonable request.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This manuscript is the authors' original work and has not been published elsewhere.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have checked the manuscript and have agreed to the submission.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We also declare that we do not have any conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work is not funded by any external or internal or any government funded agency.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The dataset analysed during the current study are available in the internet repository, and can also be made available from the corresponding author on reasonable request","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}