{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:14:02Z","timestamp":1772122442601,"version":"3.50.1"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031569494","type":"print"},{"value":"9783031569500","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-56950-0_16","type":"book-chapter","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T03:01:57Z","timestamp":1711594917000},"page":"184-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Classification of Eye Disorders Using Deep Learning and Machine Learning Models"],"prefix":"10.1007","author":[{"given":"Manal","family":"El Harti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saad","family":"Zaamoun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Said Jai","family":"Andaloussi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ouail","family":"Ouchetto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"issue":"11","key":"16_CR1","first-page":"3740","volume":"9","author":"JT Mandell","year":"2020","unstructured":"Mandell, J.T., Idarraga, M., Kumar, N., Galor, A.: Impact of air pollution and weather on dry eye. J. Intern. Med. 9(11), 3740 (2020)","journal-title":"J. Intern. Med."},{"issue":"6","key":"16_CR2","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1167\/iovs.04-1294","volume":"46","author":"JJ Nichols","year":"2005","unstructured":"Nichols, J.J., Ziegler, C., Mitchell, G.L., Nichols, K.K.: Self-reported dry eye disease across refractive modalities. Invest. Ophthalmol. Vis. Sci. 46(6), 1911\u20131914 (2005)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"1","key":"16_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40662-015-0026-2","volume":"2","author":"R Lee","year":"2015","unstructured":"Lee, R., Wong, T.Y., Sabanayagam, C.: Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vision 2(1), 1\u201325 (2015)","journal-title":"Eye Vision"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/S0140-6736(09)62124-3","volume":"376","author":"N Cheung","year":"2010","unstructured":"Cheung, N., Mitchell, P., Yin Wong, T.: Diabetic retinopathy. Lancet 376, 124\u2013136 (2010)","journal-title":"Lancet"},{"issue":"22","key":"16_CR5","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402\u20132410 (2016)","journal-title":"JAMA"},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.preteyeres.2012.08.003","volume":"32","author":"DC Hood","year":"2013","unstructured":"Hood, D.C., Raza, A.S., de Moraes, C.G.V., Liebmann, J.M., Ritch, R.: Glaucomatous damage of the macula. Prog. Retin. Eye Res. 32, 1\u201321 (2013)","journal-title":"Prog. Retin. Eye Res."},{"issue":"11","key":"16_CR7","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":"12","key":"16_CR8","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1016\/S2214-109X(17)30393-5","volume":"5","author":"SR Flaxman","year":"2017","unstructured":"Flaxman, S.R., et al.: Global causes of blindness and distance vision impairment 1990\u20132020: a systematic review and meta-analysis. Lancet Glob. Health 5(12), 1221\u20131234 (2017)","journal-title":"Lancet Glob. Health"},{"key":"16_CR9","first-page":"581","volume":"8","author":"MSB Zeev","year":"2014","unstructured":"Zeev, M.S.B., Miller, D.D., Latkany, R.: Diagnosis of dry eye disease and emerging technologies. Clin. Ophthalmol. 8, 581\u2013590 (2014)","journal-title":"Clin. Ophthalmol."},{"key":"16_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2020.103271","volume":"106","author":"J Vicnesh","year":"2020","unstructured":"Vicnesh, J., et al.: Thoughts concerning the application of thermogram images for automated diagnosis of dry eye\u2013a review. Infrared Phys. Technol. 106, 103271 (2020)","journal-title":"Infrared Phys. Technol."},{"issue":"4","key":"16_CR11","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1089\/jwh.2018.7041","volume":"28","author":"C Matossian","year":"2019","unstructured":"Matossian, C., McDonald, M., Donaldson, K.E., Nichols, K.K., MacIver, S., Gupta, P.K.: Dry eye disease: consideration for women\u2019s health. J. Women\u2019s Health 28(4), 502\u2013514 (2019)","journal-title":"J. Women\u2019s Health"},{"key":"16_CR12","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.compeleceng.2018.07.042","volume":"72","author":"S Wan","year":"2018","unstructured":"Wan, S., Liang, Y., Zhang, Y.: Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput. Electr. Eng. 72, 274\u2013282 (2018)","journal-title":"Comput. Electr. Eng."},{"issue":"13","key":"16_CR13","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1167\/iovs.16-19964","volume":"57","author":"MD Abr\u00e0moff","year":"2016","unstructured":"Abr\u00e0moff, M.D., et al.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Ophthalmol. Vis. Sci. 57(13), 5200\u20135206 (2016)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, Y., Wong, D.W.K., Wong, T.Y., Liu, J.: Glaucoma detection based on deep convolutional neural network. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 715\u2013718. IEEE (2015)","DOI":"10.1109\/EMBC.2015.7318462"},{"issue":"9","key":"16_CR15","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.1016\/j.ophtha.2016.05.029","volume":"123","author":"R Asaoka","year":"2016","unstructured":"Asaoka, R., Murata, H., Iwase, A., Araie, M.: Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology 123(9), 1974\u20131980 (2016)","journal-title":"Ophthalmology"},{"issue":"18","key":"16_CR16","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1001\/jama.2014.3192","volume":"311","author":"RN Weinreb","year":"2014","unstructured":"Weinreb, R.N., Aung, T., Medeiros, F.A.: The pathophysiology and treatment of glaucoma: a review. JAMA 311(18), 1901\u20131911 (2014)","journal-title":"JAMA"},{"issue":"9","key":"16_CR17","doi-asserted-by":"publisher","first-page":"3152","DOI":"10.1167\/iovs.04-0227","volume":"45","author":"RS Harwerth","year":"2004","unstructured":"Harwerth, R.S., Carter-Dawson, L., Smith, E.L., Barnes, G., Holt, W.F., Crawford, M.L.: Neural losses correlated with visual losses in clinical perimetry. Invest. Ophthalmol. Vis. Sci. 45(9), 3152\u20133160 (2004)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"10","key":"16_CR18","first-page":"2242","volume":"40","author":"RS Harwerth","year":"1999","unstructured":"Harwerth, R.S., Carter-Dawson, L., Shen, F., Smith, E.L., Crawford, M.L.J.: Ganglion cell losses underlying visual field defects from experimental glaucoma. Invest. Ophthalmol. Vis. Sci. 40(10), 2242\u20132250 (1999)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"16_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.07.006","volume":"182","author":"H Zhang","year":"2019","unstructured":"Zhang, H., Niu, K., Xiong, Y., Yang, W., He, Z., Song, H.: Automatic cataract grading methods based on deep learning. Comput. Methods Programs Biomed. 182, 104978 (2019)","journal-title":"Comput. Methods Programs Biomed."},{"key":"16_CR20","doi-asserted-by":"publisher","first-page":"128799","DOI":"10.1109\/ACCESS.2021.3112938","volume":"9","author":"MS Junayed","year":"2021","unstructured":"Junayed, M.S., Islam, M.B., Sadeghzadeh, A., Rahman, S.: CataractNet: an automated cataract detection system using deep learning for fundus images. IEEE Access 9, 128799\u2013128808 (2021)","journal-title":"IEEE Access"},{"key":"16_CR21","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.jtos.2021.11.004","volume":"23","author":"AM Stor\u00e5s","year":"2022","unstructured":"Stor\u00e5s, A.M., et al.: Artificial intelligence in dry eye disease. Ocul. Surf. 23, 74\u201386 (2022)","journal-title":"Ocul. Surf."},{"issue":"3","key":"16_CR22","doi-asserted-by":"publisher","first-page":"28","DOI":"10.35119\/maio.v2i3.90","volume":"2","author":"K Yabusaki","year":"2019","unstructured":"Yabusaki, K., Arita, R., Yamauchi, T.: Automated classification of dry eye type analyzing interference fringe color images of tear film using machine learning techniques. Model. Artif. Intell. Ophthalmol. 2(3), 28\u201335 (2019)","journal-title":"Model. Artif. Intell. Ophthalmol."},{"key":"16_CR23","doi-asserted-by":"publisher","first-page":"4281","DOI":"10.2147\/OPTH.S321764","volume":"15","author":"C Chase","year":"2021","unstructured":"Chase, C., Elsawy, A., Eleiwa, T., Ozcan, E., Tolba, M., Abou Shousha, M.: Comparison of autonomous AS-OCT deep learning algorithm and clinical dry eye tests in diagnosis of dry eye disease. Clin. Ophthalmol. 15, 4281\u20134289 (2021)","journal-title":"Clin. Ophthalmol."},{"key":"16_CR24","first-page":"1","volume":"2015","author":"SP Phadatare","year":"2015","unstructured":"Phadatare, S.P., Momin, M., Nighojkar, P., Askarkar, S., Singh, K.K.: A comprehensive review on dry eye disease: diagnosis, medical management, recent developments, and future challenges. Adv. Pharm. 2015, 1\u201312 (2015)","journal-title":"Adv. Pharm."},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Shao, Y., et al.: Detection of meibomian gland dysfunction by in vivo confocal microscopy based on the deep convolutional neural network. Res. Square 1 (2021)","DOI":"10.21203\/rs.3.rs-936418\/v1"},{"issue":"4","key":"16_CR26","doi-asserted-by":"publisher","first-page":"1922","DOI":"10.1167\/iovs.10-6997a","volume":"52","author":"KK Nichols","year":"2011","unstructured":"Nichols, K.K., et al.: The international workshop on meibomian gland dysfunction: executive summary. Invest. Ophthalmol. Vis. Sci. 52(4), 1922\u20131929 (2011)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"8","key":"16_CR27","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3390\/diagnostics10080559","volume":"10","author":"Y Okumura","year":"2020","unstructured":"Okumura, Y., et al.: A review of dry eye questionnaires: measuring patient-reported outcomes and health-related quality of life. Diagnostics 10(8), 559\u2013567 (2020)","journal-title":"Diagnostics"},{"issue":"7","key":"16_CR28","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.3390\/diagnostics11071246","volume":"11","author":"N Hung","year":"2021","unstructured":"Hung, N., et al.: Using slit-lamp images for deep learning-based identification of bacterial and fungal keratitis: model development and validation with different convolutional neural networks. Diagnostics 11(7), 1246\u20131251 (2021)","journal-title":"Diagnostics"},{"issue":"16","key":"16_CR29","doi-asserted-by":"publisher","first-page":"6857","DOI":"10.1109\/JSEN.2018.2850940","volume":"18","author":"TY Su","year":"2018","unstructured":"Su, T.Y., Liu, Z.Y., Chen, D.Y.: Tear film break-up time measurement using deep convolutional neural networks for screening dry eye disease. IEEE Sens. J. 18(16), 6857\u20136862 (2018)","journal-title":"IEEE Sens. J."},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Glaretsubin, P., Muthukannan, P.: Optimized convolution neural network based multiple eye disease detection. Comput. Biol. Med. 146 (2022)","DOI":"10.1016\/j.compbiomed.2022.105648"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Raza, A., Khan, M.U., Saeed, Z., Samer, S., Mobeen, A., Samer, A.: Classification of eye diseases and detection of cataract using digital fundus imaging (DFI) and inception-V4 deep learning model. In: 2021 International Conference on Frontiers of Information Technology (FIT), pp. 137\u2013142. IEEE (2021)","DOI":"10.1109\/FIT53504.2021.00034"},{"key":"16_CR32","doi-asserted-by":"publisher","unstructured":"Orfao, J., van der Haar, D.: A comparison of computer vision methods for the combined detection of glaucoma, diabetic retinopathy and cataracts. In: Papie\u017c, B.W., Yaqub, M., Jiao, J., Namburete, A.I.L., Noble, J.A. (eds.) Medical Image Understanding and Analysis (MIUA 2021). LNCS, vol. 12722, pp. 30\u201342. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-80432-9_3","DOI":"10.1007\/978-3-030-80432-9_3"},{"issue":"2","key":"16_CR33","first-page":"42","volume":"2","author":"G Arslan","year":"2023","unstructured":"Arslan, G., Erda\u015f, \u00c7.B.: Detection of cataract, diabetic retinopathy and glaucoma eye diseases with deep learning approach. Intell. Methods Eng. Sci. 2(2), 42\u201347 (2023)","journal-title":"Intell. Methods Eng. Sci."},{"issue":"6","key":"16_CR34","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.1109\/JBHI.2022.3214086","volume":"27","author":"MA Rodr\u00edguez","year":"2023","unstructured":"Rodr\u00edguez, M.A., AlMarzouqi, H., Liatsis, P.: Multi-label retinal disease classification using transformers. IEEE J. Biomed. Health Inform. 27(6), 2739\u20132750 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"16_CR35","doi-asserted-by":"crossref","unstructured":"de Raad, K.B., et al.: The effect of preprocessing on convolutional neural networks for medical image segmentation. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 655\u2013658 (2021)","DOI":"10.1109\/ISBI48211.2021.9433952"},{"key":"16_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101776","volume":"57","author":"SM Prabhu","year":"2020","unstructured":"Prabhu, S.M., Chakiat, A., Shashank, S., Vunnava, K.P., Shetty, R.: Deep learning segmentation and quantification of Meibomian glands. Biomed. Signal Process. Control 57, 101776 (2020)","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"16_CR37","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1109\/JSEN.2019.2948576","volume":"20","author":"TY Su","year":"2019","unstructured":"Su, T.Y., Ting, P.J., Chang, S.W., Chen, D.Y.: Superficial punctate keratitis grading for dry eye screening using deep convolutional neural networks. IEEE Sens. J. 20(3), 1672\u20131678 (2019)","journal-title":"IEEE Sens. J."}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Second International Conference on Advances in Computing Research (ACR\u201924)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56950-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T03:12:20Z","timestamp":1711595540000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56950-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031569494","9783031569500"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56950-0_16","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Computing Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acr2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iicser.org\/ACR24","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}