{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T19:53:10Z","timestamp":1780602790605,"version":"3.54.1"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030515164","type":"print"},{"value":"9783030515171","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T00:00:00Z","timestamp":1592870400000},"content-version":"vor","delay-in-days":174,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-51517-1_1","type":"book-chapter","created":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T14:03:30Z","timestamp":1593007410000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Alzheimer\u2019s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture"],"prefix":"10.1007","author":[{"given":"Braulio","family":"Solano-Rojas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo","family":"Villal\u00f3n-Fonseca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriela","family":"Mar\u00edn-Ravent\u00f3s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,23]]},"reference":[{"key":"1_CR1","unstructured":"Alzheimer\u2019s Disease Neuroimaging Initiative: Study Design (2017). \nhttp:\/\/adni.loni.usc.edu\/study-design\/"},{"key":"1_CR2","doi-asserted-by":"publisher","unstructured":"B\u00e4ckstr\u00f6m, K., Nazari, M., Gu, I.Y., Jakola, A.S.: An efficient 3D deep convolutional network for Alzheimer\u2019s disease diagnosis using MR images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 149\u2013153, April 2018. \nhttps:\/\/doi.org\/10.1109\/ISBI.2018.8363543","DOI":"10.1109\/ISBI.2018.8363543"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"61677","DOI":"10.1109\/ACCESS.2018.2874767","volume":"6","author":"T Carneiro","year":"2018","unstructured":"Carneiro, T., Medeiros Da N\u00f3Brega, R.V., Nepomuceno, T., Bian, G., De Albuquerque, V.H.C., Filho, P.P.R.: Performance analysis of Google colaboratory as a tool for accelerating deep learning applications. IEEE Access 6, 61677\u201361685 (2018). \nhttps:\/\/doi.org\/10.1109\/ACCESS.2018.2874767","journal-title":"IEEE Access"},{"key":"1_CR4","doi-asserted-by":"publisher","unstructured":"Cheng, D., Liu, M.: CNNs based multi-modality classification for AD diagnosis. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1\u20135, October 2017. \nhttps:\/\/doi.org\/10.1109\/CISP-BMEI.2017.8302281","DOI":"10.1109\/CISP-BMEI.2017.8302281"},{"key":"1_CR5","unstructured":"Cohen, J.P., Bertin, P., Frappier, V.: Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System. \narXiv:1901.11210\n\n (2019)"},{"issue":"5","key":"1_CR6","doi-asserted-by":"publisher","first-page":"2099","DOI":"10.1109\/JBHI.2018.2882392","volume":"23","author":"R Cui","year":"2019","unstructured":"Cui, R., Liu, M.: Hippocampus analysis by combination of 3-D DenseNet and shapes for Alzheimer\u2019s disease diagnosis. IEEE J. Biomed. Health Inform. 23(5), 2099\u20132107 (2019). \nhttps:\/\/doi.org\/10.1109\/JBHI.2018.2882392","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Graber, M., Franklin, N.: Diagnostic error in internal medicine. Arch. Intern. Med. 165 (2005). \nhttps:\/\/doi.org\/10.1001\/archinte.165.13.1493","DOI":"10.1001\/archinte.165.13.1493"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6546\u20136555 (2018)","DOI":"10.1109\/CVPR.2018.00685"},{"key":"1_CR9","doi-asserted-by":"publisher","unstructured":"He, G., Ping, A., Wang, X., Zhu, Y.: Alzheimer\u2019s disease diagnosis model based on three-dimensional full convolutional DenseNet. In: 2019 10th International Conference on Information Technology in Medicine and Education (ITME), pp. 13\u201317, August 2019. \nhttps:\/\/doi.org\/10.1109\/ITME.2019.00014","DOI":"10.1109\/ITME.2019.00014"},{"key":"1_CR10","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 2261\u20132269 (2017). \nhttps:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"1_CR11","doi-asserted-by":"publisher","unstructured":"Jabason, E., Ahmad, M.O., Swamy, M.N.S.: Classification of Alzheimer\u2019s disease from MRI data using an ensemble of hybrid deep convolutional neural networks. In: 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 481\u2013484, August 2019. \nhttps:\/\/doi.org\/10.1109\/MWSCAS.2019.8884939","DOI":"10.1109\/MWSCAS.2019.8884939"},{"key":"1_CR12","doi-asserted-by":"publisher","unstructured":"Khan, S., Rahmani, H., Shah, S., Bennamoun, M.: A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision, No. 1. Morgan & Claypool Publishers (2018). \nhttps:\/\/doi.org\/10.2200\/S00822ED1V01Y201712COV015","DOI":"10.2200\/S00822ED1V01Y201712COV015"},{"issue":"2","key":"1_CR13","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s40747-017-0064-6","volume":"4","author":"D Kollias","year":"2017","unstructured":"Kollias, D., Tagaris, A., Stafylopatis, A., Kollias, S., Tagaris, G.: Deep neural architectures for prediction in healthcare. Complex Intell. Syst. 4(2), 119\u2013131 (2017). \nhttps:\/\/doi.org\/10.1007\/s40747-017-0064-6","journal-title":"Complex Intell. Syst."},{"issue":"3","key":"1_CR14","doi-asserted-by":"publisher","first-page":"611","DOI":"10.2214\/AJR.12.10375","volume":"201","author":"CS Lee","year":"2013","unstructured":"Lee, C.S., Nagy, P.G., Weaver, S.J., Newman-Toker, D.E.: Cognitive and system factors contributing to diagnostic errors in radiology. Am. J. Roentgenol. 201(3), 611\u2013617 (2013). \nhttps:\/\/doi.org\/10.2214\/AJR.12.10375","journal-title":"Am. J. Roentgenol."},{"key":"1_CR15","unstructured":"National Institute of Biomedical Imaging and Bioengineering: Magnetic Resonance Imaging (MRI). \nhttps:\/\/www.nibib.nih.gov\/science-education\/science-topics\/magnetic-resonance-imaging-mri"},{"key":"1_CR16","unstructured":"National Institute of Biomedical Imaging and Bioengineering: Nuclear Medicine. \nhttps:\/\/www.nibib.nih.gov\/science-education\/science-topics\/nuclear-medicine#pid-1001"},{"key":"1_CR17","unstructured":"National Institute on Aging: Alzheimer\u2019s Disease Fact Sheet. \nhttps:\/\/www.nia.nih.gov\/health\/alzheimers-disease-fact-sheet\n\n. Accessed 13 May 2018"},{"key":"1_CR18","unstructured":"Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. CoRR abs\/1711.05225 (2017). \nhttp:\/\/arxiv.org\/abs\/1711.05225"},{"issue":"03","key":"1_CR19","doi-asserted-by":"publisher","first-page":"1850011","DOI":"10.1142\/S0218213018500112","volume":"27","author":"A Tagaris","year":"2018","unstructured":"Tagaris, A., Kollias, D., Stafylopatis, A., Tagaris, G., Kollias, S.: Machine learning for neurodegenerative disorder diagnosis \u2014 survey of practices and launch of benchmark dataset. Int. J. Artif. Intell. Tools 27(03), 1850011 (2018). \nhttps:\/\/doi.org\/10.1142\/S0218213018500112","journal-title":"Int. J. Artif. Intell. Tools"}],"container-title":["Lecture Notes in Computer Science","The Impact of Digital Technologies on Public Health in Developed and Developing Countries"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-51517-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T14:14:45Z","timestamp":1593008085000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-51517-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030515164","9783030515171"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-51517-1_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICOST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Smart Homes and Health Telematics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hammamet","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tunisia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2020","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":"icost2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icost-society.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"49","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":"17","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":"23","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":"35% - 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":"2","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":"3","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}