{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:26:43Z","timestamp":1742948803854,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031407246"},{"type":"electronic","value":"9783031407253"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-40725-3_58","type":"book-chapter","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T23:02:46Z","timestamp":1693263766000},"page":"685-697","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Convolutional Neural Networks for\u00a0Diabetic Retinopathy Grading from\u00a0iPhone Fundus Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2602-5773","authenticated-orcid":false,"given":"Samuel","family":"Lozano-Ju\u00e1rez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2988-757X","authenticated-orcid":false,"given":"Nuria","family":"Velasco-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5590-9188","authenticated-orcid":false,"given":"Ian","family":"Roberts","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7298-9060","authenticated-orcid":false,"given":"Jer\u00f3nimo","family":"Bernal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7289-4689","authenticated-orcid":false,"given":"Nu\u00f1o","family":"Basurto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2662-798X","authenticated-orcid":false,"given":"Daniel","family":"Urda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2444-5384","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Herrero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,29]]},"reference":[{"issue":"1","key":"58_CR1","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.jmir.2022.11.016","volume":"54","author":"TN Akudjedu","year":"2023","unstructured":"Akudjedu, T.N., Torre, S., Khine, R., Katsifarakis, D., Newman, D., Malamateniou, C.: Knowledge, perceptions, and expectations of artificial intelligence in radiography practice: a global radiography workforce survey. J. Med. Imaging Radiat. Sci. 54(1), 104\u2013116 (2023)","journal-title":"J. Med. Imaging Radiat. Sci."},{"key":"58_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1088-1","volume":"42","author":"SM Anwar","year":"2018","unstructured":"Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K.: Medical image analysis using convolutional neural networks: a review. J. Med. Syst. 42, 1\u201313 (2018)","journal-title":"J. Med. Syst."},{"issue":"6","key":"58_CR3","doi-asserted-by":"publisher","first-page":"658","DOI":"10.3129\/i08-120","volume":"43","author":"MC Boucher","year":"2008","unstructured":"Boucher, M.C., et al.: Teleophthalmology screening for diabetic retinopathy through mobile imaging units within Canada. Can. J. Ophthalmol. 43(6), 658\u2013668 (2008)","journal-title":"Can. J. Ophthalmol."},{"key":"58_CR4","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"issue":"3","key":"58_CR5","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1046\/j.1442-9071.2000.00309.x","volume":"28","author":"I Constable","year":"2000","unstructured":"Constable, I., Yogesan, K., Eikelboom, R., Barry, C., Cuypers, M.: Fred hollows lecture: digital screening for eye disease. Clin. Exp. Ophthalmol. 28(3), 129\u2013132 (2000)","journal-title":"Clin. Exp. Ophthalmol."},{"key":"58_CR6","doi-asserted-by":"crossref","unstructured":"Esteva, A., et al.: Deep learning-enabled medical computer vision. NPJ Digit. Med. 4(1), 1\u20139 (2021)","DOI":"10.1038\/s41746-020-00376-2"},{"issue":"12","key":"58_CR7","doi-asserted-by":"publisher","first-page":"e1221","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), e1221\u2013e1234 (2017)","journal-title":"Lancet Glob. Health"},{"key":"58_CR8","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, C.B., Souza, J.R., Fernandes, H.: CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images. Comput. Biol. Med. 142, 105205 (2022)","DOI":"10.1016\/j.compbiomed.2021.105205"},{"key":"58_CR9","doi-asserted-by":"crossref","unstructured":"Group, D.R.: Frequency of evidence-based screening for retinopathy in type 1 diabetes. N. Engl. J. Med. 376(16), 1507\u20131516 (2017)","DOI":"10.1056\/NEJMoa1612836"},{"key":"58_CR10","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2018","unstructured":"Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354\u2013377 (2018)","journal-title":"Pattern Recognit."},{"key":"58_CR11","doi-asserted-by":"crossref","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402\u20132410 (2016)","DOI":"10.1001\/jama.2016.17216"},{"key":"58_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"58_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. CoRR abs\/1603.05027 (2016)","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"58_CR14","unstructured":"International Diabetes Federation: IDF diabetes atlas 2019 (2019). https:\/\/www.diabetesatlas.org\/en\/. Accessed 2 Mar 2020"},{"key":"58_CR15","doi-asserted-by":"crossref","unstructured":"Karatzia, L., Aung, N., Aksentijevic, D.: Artificial intelligence in cardiology: hope for the future and power for the present. Front. Cardiovasc. Med. 9 (2022)","DOI":"10.3389\/fcvm.2022.945726"},{"key":"58_CR16","unstructured":"Krishna, S.T., Kalluri, H.K.: Deep learning and transfer learning approaches for image classification. Int. J. Recent Technol. Eng. (IJRTE) 7(5S4), 427\u2013432 (2019)"},{"key":"58_CR17","doi-asserted-by":"crossref","unstructured":"Kuo, R.Y., et al.: Artificial intelligence in fracture detection: a systematic review and meta-analysis. Radiology 304(1), 50\u201362 (2022). pMID: 35348381","DOI":"10.1148\/radiol.211785"},{"key":"58_CR18","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s10654-019-00560-z","volume":"35","author":"JQ Li","year":"2020","unstructured":"Li, J.Q., et al.: Prevalence, incidence and future projection of diabetic eye disease in Europe: a systematic review and meta-analysis. Eur. J. Epidemiol. 35, 11\u201323 (2020)","journal-title":"Eur. J. Epidemiol."},{"key":"58_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106750","volume":"157","author":"H Liu","year":"2023","unstructured":"Liu, H., Teng, L., Fan, L., Sun, Y., Li, H.: A new ultra-wide-field fundus dataset to diabetic retinopathy grading using hybrid preprocessing methods. Comput. Biol. Med. 157, 106750 (2023)","journal-title":"Comput. Biol. Med."},{"issue":"9","key":"58_CR20","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.3390\/diagnostics12092262","volume":"12","author":"K Mujeeb Rahman","year":"2022","unstructured":"Mujeeb Rahman, K., Nasor, M., Imran, A.: Automatic screening of diabetic retinopathy using fundus images and machine learning algorithms. Diagnostics 12(9), 2262 (2022)","journal-title":"Diagnostics"},{"key":"58_CR21","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.diabres.2017.03.024","volume":"128","author":"K Ogurtsova","year":"2017","unstructured":"Ogurtsova, K., et al.: IDF diabetes atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res. Clin. Pract. 128, 40\u201350 (2017)","journal-title":"Diabetes Res. Clin. Pract."},{"key":"58_CR22","unstructured":"of Ophthalmology, A.A.: Diabetic retinopathy ppp - updated 2017. https:\/\/www.aao.org\/preferred-practice-pattern\/diabetic-retinopathy-ppp-updated-2017. Accessed 22 Jan 2020"},{"key":"58_CR23","doi-asserted-by":"crossref","unstructured":"Piccialli, F., Somma, V.D., Giampaolo, F., Cuomo, S., Fortino, G.: A survey on deep learning in medicine: why, how and when? Inf. Fusion 66, 111\u2013137 (2021)","DOI":"10.1016\/j.inffus.2020.09.006"},{"key":"58_CR24","doi-asserted-by":"crossref","unstructured":"Pranav, R., Emma, C., Oishi, B., J., T.E.: AI in health and medicine. Nat. Med. 28, 31\u201338 (2022)","DOI":"10.1038\/s41591-021-01614-0"},{"key":"58_CR25","doi-asserted-by":"crossref","unstructured":"Qin, X., Chen, D., Zhan, Y., Yin, D.: Classification of diabetic retinopathy based on improved deep forest model. Biomed. Signal Process. Control 79, 104020 (2023)","DOI":"10.1016\/j.bspc.2022.104020"},{"issue":"6","key":"58_CR26","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1136\/bjophthalmol-2014-305631","volume":"99","author":"L Shi","year":"2015","unstructured":"Shi, L., Wu, H., Dong, J., Jiang, K., Lu, X., Shi, J.: Telemedicine for detecting diabetic retinopathy: a systematic review and meta-analysis. Br. J. Ophthalmol. 99(6), 823\u2013831 (2015)","journal-title":"Br. J. Ophthalmol."},{"key":"58_CR27","doi-asserted-by":"crossref","unstructured":"Tran, B.X., et al.: Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J. Clin. Med. 8(3), 360 (2019)","DOI":"10.3390\/jcm8030360"},{"key":"58_CR28","doi-asserted-by":"crossref","unstructured":"Yiming, Z., Ying, W., Jonathan, L.: Applications of explainable artificial intelligence in diagnosis and surgery. Diagnostics 12, 237 (2022)","DOI":"10.3390\/diagnostics12020237"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40725-3_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T00:02:38Z","timestamp":1729987358000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40725-3_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031407246","9783031407253"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40725-3_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2023.haisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"54% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}