{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T04:08:21Z","timestamp":1750738101574,"version":"3.41.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031962547","type":"print"},{"value":"9783031962554","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-96255-4_25","type":"book-chapter","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T02:14:12Z","timestamp":1750731252000},"page":"269-278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Approaches for\u00a0Glaucoma Detection: A Comparative Study of\u00a0CNN Models on\u00a0Retinal Fundus Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8151-2654","authenticated-orcid":false,"given":"Eugenia","family":"Arrieta Rodr\u00edguez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4156-7105","authenticated-orcid":false,"given":"Jos\u00e9","family":"Araque-Gallardo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5430-5438","authenticated-orcid":false,"given":"Oscar Luis","family":"Teheran Forero","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4926-7414","authenticated-orcid":false,"given":"Emiro","family":"De-La-Hoz-Franco","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0518-3187","authenticated-orcid":false,"given":"Jos\u00e9","family":"Escorcia-Gutierrez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"issue":"1","key":"25_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.survophthal.2022.08.005","volume":"68","author":"LJ Coan","year":"2023","unstructured":"Coan, L.J., et al.: Automatic detection of glaucoma via fundus imaging and artificial intelligence: a review. Surv. Ophthalmol. 68(1), 17\u201341 (2023)","journal-title":"Surv. Ophthalmol."},{"issue":"1","key":"25_CR2","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12938-019-0649-y","volume":"18","author":"A D\u00edaz-Pinto","year":"2019","unstructured":"D\u00edaz-Pinto, A., Morales, S., Naranjo, V., K\u00f6hler, T., Mossi, J.M., Navea, A.: CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed. Eng. Online 18(1), 29 (2019). https:\/\/doi.org\/10.1186\/s12938-019-0649-y","journal-title":"Biomed. Eng. Online"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Escorcia-Gutierrez, J., et\u00a0al.: Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images. In: Lecture Notes in Computer Science, vol. 13293, pp. 202\u2013213 (2022)","DOI":"10.1007\/978-3-031-10539-5_15"},{"key":"25_CR4","unstructured":"Escorcia-Gutierrez, J., et\u00a0al.: Grading diabetic retinopathy using transfer learning-based convolutional neural networks. In: Lecture Notes in Computer Science, vol. 13293, pp. 214\u2013225 (2022)"},{"issue":"8","key":"25_CR5","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"issue":"4","key":"25_CR6","first-page":"155","volume":"47","author":"MJ Girard","year":"2012","unstructured":"Girard, M.J., Strouthidis, N.G., Ethier, C.R., Mari, J.M.: Imaging the visual pathway: from the eye to the brain. Ophthalmic Res. 47(4), 155\u2013160 (2012)","journal-title":"Ophthalmic Res."},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR8","doi-asserted-by":"publisher","first-page":"100261","DOI":"10.1016\/j.health.2023.100261","volume":"4","author":"AE Ilesanmi","year":"2023","unstructured":"Ilesanmi, A.E., Ilesanmi, T., Gbotoso, G.A.: A systematic review of retinal fundus image segmentation and classification methods using convolutional neural networks. Healthc. Analyt. 4, 100261 (2023)","journal-title":"Healthc. Analyt."},{"issue":"2","key":"25_CR9","doi-asserted-by":"publisher","first-page":"434","DOI":"10.3390\/s22020434","volume":"22","author":"MA Khan","year":"2022","unstructured":"Khan, M.A., Cha, J.: An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization. Sensors 22(2), 434 (2022). https:\/\/doi.org\/10.3390\/s22020434","journal-title":"Sensors"},{"issue":"2","key":"25_CR10","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1109\/TMI.2019.2927226","volume":"39","author":"L Li","year":"2020","unstructured":"Li, L., et al.: A large-scale database and a CNN model for attention-based glaucoma detection. IEEE Trans. Med. Imaging 39(2), 413\u2013424 (2020). https:\/\/doi.org\/10.1109\/TMI.2019.2927226","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"25_CR11","first-page":"105","volume":"52","author":"FA Medeiros","year":"2011","unstructured":"Medeiros, F.A., Zangwill, L.M., Bowd, C., Vessani, R.M., Susanna, R., Jr., Weinreb, R.N.: Detection of glaucoma progression with stratus OCT retinal nerve fiber layer, optic nerve head, and macular thickness measurements. Invest. Ophthalmol. Vis. Sci. 52(1), 105\u2013113 (2011)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"25_CR12","doi-asserted-by":"publisher","unstructured":"Raju, M., Shanmugam, K.P., Shyu, C.R.: Application of machine learning predictive models for early detection of glaucoma using real world data. Appl. Sci. 13(4) (2023). https:\/\/doi.org\/10.3390\/app13042445","DOI":"10.3390\/app13042445"},{"issue":"23","key":"25_CR13","doi-asserted-by":"publisher","first-page":"12722","DOI":"10.3390\/app132312722","volume":"13","author":"JF Sigut","year":"2023","unstructured":"Sigut, J.F., D\u00edaz-Alem\u00e1n, T.: Comparison of the performance of convolutional neural networks and vision transformer-based systems for automated glaucoma detection with eye fundus images. Appl. Sci. 13(23), 12722 (2023). https:\/\/doi.org\/10.3390\/app132312722","journal-title":"Appl. Sci."},{"key":"25_CR14","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint: arXiv:1409.1556 (2014)"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a01\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"11","key":"25_CR17","doi-asserted-by":"publisher","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","volume":"121","author":"YC Tham","year":"2014","unstructured":"Tham, Y.C., Li, X., Wong, T.Y., Quigley, H.A., Aung, T., Cheng, C.Y.: Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11), 2081\u20132090 (2014)","journal-title":"Ophthalmology"},{"issue":"1","key":"25_CR18","first-page":"3971516","volume":"2022","author":"A Zafar","year":"2022","unstructured":"Zafar, A., Aamir, M., Nawi, N.M., Ali, S., Husnain, M., Samad, A.: A comprehensive convolutional neural network survey to detect glaucoma disease. Mob. Inf. Syst. 2022(1), 3971516 (2022)","journal-title":"Mob. Inf. Syst."},{"issue":"13","key":"25_CR19","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.3390\/diagnostics13132180","volume":"13","author":"MJ Zedan","year":"2023","unstructured":"Zedan, M.J., Zulkifley, M.A., Ibrahim, A.A., Moubark, A.M., Kamari, N., Abdani, S.R.: Automated glaucoma screening and diagnosis based on retinal fundus images using deep learning approaches: A comprehensive review. Diagnostics 13(13), 2180 (2023)","journal-title":"Diagnostics"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96255-4_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T02:14:16Z","timestamp":1750731256000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96255-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031962547","9783031962554"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96255-4_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"18 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guanajuato","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mcpr22025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mcpr2025.eventos.cimat.mx\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}