{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T07:12:12Z","timestamp":1774163532780,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030963071","type":"print"},{"value":"9783030963088","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-96308-8_27","type":"book-chapter","created":{"date-parts":[[2022,3,26]],"date-time":"2022-03-26T13:15:41Z","timestamp":1648300541000},"page":"296-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["ResD Hybrid Model Based on\u00a0Resnet18 and\u00a0Densenet121 for\u00a0Early Alzheimer Disease Classification"],"prefix":"10.1007","author":[{"given":"Modupe","family":"Odusami","sequence":"first","affiliation":[]},{"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[]},{"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[]},{"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,27]]},"reference":[{"issue":"1","key":"27_CR1","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/S1474-4422(18)30403-4","volume":"18","author":"E Nichols","year":"2019","unstructured":"Nichols, E., Szoeke, C.E.I., et al.: Global, regional, and national burden of Alzheimer\u2019s disease and other dementias, 1990\u20132016: a systematic analysis for the global burden of disease study 2016. Lancet Neurol. 18(1), 88\u2013106 (2019)","journal-title":"Lancet Neurol."},{"key":"27_CR2","doi-asserted-by":"publisher","first-page":"206","DOI":"10.3389\/fnagi.2020.00206","volume":"12","author":"L Kang","year":"2020","unstructured":"Kang, L., Jiang, J., Huang, J., Zhang, T.: Identifying early mild cognitive impairment by multi-modality MRI-based deep learning. Front. Aging Neurosci. 12, 206 (2020)","journal-title":"Front. Aging Neurosci."},{"key":"27_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neulet.2020.134971","volume":"730","author":"J Jiang","year":"2020","unstructured":"Jiang, J., Kang, L., Huang, J., Zhang, T.: Deep learning based mild cognitive impairment diagnosis using structure MR images. Neurosci. Lett. 730, 134971 (2020)","journal-title":"Neurosci. Lett."},{"issue":"6","key":"27_CR4","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.3390\/diagnostics11061071","volume":"11","author":"M Odusami","year":"2021","unstructured":"Odusami, M., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R., Krilavi\u010dius, T.: Analysis of features of Alzheimer\u2019s disease: detection of early stage from functional brain changes in magnetic resonance images using a finetuned resnet18 network. Diagnostics 11(6), 1071 (2021)","journal-title":"Diagnostics"},{"key":"27_CR5","doi-asserted-by":"publisher","first-page":"777","DOI":"10.3389\/fnins.2018.00777","volume":"12","author":"W Lin","year":"2018","unstructured":"Lin, W., et al.: Convolutional neural networks-based MRI image analysis for the Alzheimer\u2019s disease prediction from mild cognitive impairment. Front. Neurosci. 12, 777 (2018)","journal-title":"Front. Neurosci."},{"key":"27_CR6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neuroscience.2021.01.002","volume":"460","author":"A Mehmood","year":"2021","unstructured":"Mehmood, A., et al.: A transfer learning approach for early diagnosis of Alzheimer\u2019s disease on MRI images. Neuroscience 460, 43\u201352 (2021)","journal-title":"Neuroscience"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Nawaz, A., Anwar, S.M., Liaqat, R., Iqbal, J., Bagci, U., Majid, M.: Deep convolutional neural network based classification of Alzheimer\u2019s disease using MRI data. In: 2020 IEEE 23rd International Multitopic Conference (INMIC). IEEE (2020)","DOI":"10.1109\/INMIC50486.2020.9318172"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Hon, M., Khan, N.M.: Towards Alzheimer\u2019s disease classification through transfer learning, pp. 1166\u20131169 (2017)","DOI":"10.1109\/BIBM.2017.8217822"},{"issue":"04","key":"27_CR9","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.irbm.2020.06.006","volume":"42","author":"RR Janghel","year":"2021","unstructured":"Janghel, R.R., Rathore, Y.K.: Deep convolution neural network based system for early diagnosis of Alzheimer\u2019s disease. IRBM 42(04), 258\u2013267 (2021)","journal-title":"IRBM"},{"key":"27_CR10","unstructured":"Islam, J., Zhang, Y.: Understanding 3D CNN behavior for Alzheimer\u2019s disease diagnosis from brain pet scan (2019)"},{"issue":"2","key":"27_CR11","doi-asserted-by":"publisher","first-page":"84","DOI":"10.3390\/brainsci10020084","volume":"10","author":"A Mehmood","year":"2020","unstructured":"Mehmood, A., Maqsood, M., Bashir, M., Shuyuan, Y.: A deep Siamese convolution neural network for multi-class classification of Alzheimer disease. Brain Sci. 10(2), 84 (2020)","journal-title":"Brain Sci."},{"issue":"11","key":"27_CR12","doi-asserted-by":"publisher","first-page":"2645","DOI":"10.3390\/s19112645","volume":"19","author":"M Maqsood","year":"2019","unstructured":"Maqsood, M., et al.: Transfer learning assisted classification and detection of Alzheimer\u2019s disease stages using 3D MRI scans. Sensors 19(11), 2645 (2019)","journal-title":"Sensors"},{"key":"27_CR13","doi-asserted-by":"publisher","first-page":"103764","DOI":"10.1016\/j.compbiomed.2020.103764","volume":"120","author":"A Puente-Castro","year":"2020","unstructured":"Puente-Castro, A., Fernandez-Blanco, E., Pazos, A., Munteanu, C.R.: Automatic assessment of Alzheimer\u2019s disease diagnosis based on deep learning techniques. Comput. Biol. Med. 120, 103764 (2020)","journal-title":"Comput. Biol. Med."},{"issue":"20","key":"27_CR14","doi-asserted-by":"publisher","first-page":"30117","DOI":"10.1007\/s11042-020-10331-8","volume":"80","author":"A Ashraf","year":"2021","unstructured":"Ashraf, A., Naz, S., Shirazi, S.H., Razzak, I., Parsad, M.: Deep transfer learning for Alzheimer neurological disorder detection. Multimedia Tools Appl. 80(20), 30117\u201330142 (2021)","journal-title":"Multimedia Tools Appl."},{"issue":"2","key":"27_CR15","first-page":"1","volume":"44","author":"F Ramzan","year":"2019","unstructured":"Ramzan, F., et al.: A deep learning approach for automated diagnosis and multi-class classification of Alzheimer\u2019s disease stages using resting-state fMRI and residual neural networks. J. Med. Syst. 44(2), 1\u201316 (2019)","journal-title":"J. Med. Syst."}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-96308-8_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,26]],"date-time":"2022-03-26T13:20:20Z","timestamp":1648300820000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-96308-8_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030963071","9783030963088"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-96308-8_27","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems Design and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isda2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/isda21\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}