{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:02:58Z","timestamp":1764842578456,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030948757"},{"type":"electronic","value":"9783030948764"}],"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\/978-3-030-94876-4_17","type":"book-chapter","created":{"date-parts":[[2022,1,18]],"date-time":"2022-01-18T08:20:02Z","timestamp":1642494002000},"page":"245-259","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-channel Deep Model for Classification of Alzheimer\u2019s Disease Using Transfer Learning"],"prefix":"10.1007","author":[{"given":"Sriram","family":"Dharwada","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jitendra","family":"Tembhurne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tausif","family":"Diwan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"17_CR1","unstructured":"http:\/\/adni.loni.usc.edu\/. Accessed 21 Feb 2021"},{"key":"17_CR2","unstructured":"https:\/\/www.oasis-brains.org\/. Accessed 21 Feb 2021"},{"key":"17_CR3","unstructured":"Alzheimer\u2019s disease - Symptoms and Causes. https:\/\/www.mayoclinic.org\/diseases-conditions\/alzheimers-disease\/symptoms-causes\/syc-20350447. Accessed 10 Feb 2021"},{"key":"17_CR4","doi-asserted-by":"publisher","first-page":"123","DOI":"10.2147\/DNND.S228939","volume":"9","author":"R Jill","year":"2019","unstructured":"Jill, R., Langerman, H.: Alzheimer\u2019s disease - why we need early diagnosis. Degenerative Neurol. Neuromuscul. Dis. 9, 123\u2013130 (2019). https:\/\/doi.org\/10.2147\/DNND.S228939","journal-title":"Degenerative Neurol. Neuromuscul. Dis."},{"issue":"4","key":"17_CR5","doi-asserted-by":"publisher","first-page":"a006213","DOI":"10.1101\/cshperspect.a006213","volume":"2","author":"A Keith","year":"2012","unstructured":"Keith, A., et al.: Brain imaging in Alzheimer disease. Cold Spring Harb. Perspect. Med. 2(4), a006213 (2012). https:\/\/doi.org\/10.1101\/cshperspect.a006213","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"17_CR6","unstructured":"Alzheimer Disease - Radiology Reference Article. https:\/\/radiopaedia.org\/articles\/alzheimer-disease-1. Accessed 10 Feb 2021"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269. IEEE (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"17_CR9","unstructured":"Simonyan K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs\/1409.1556 (2015)"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Krizhevsky, A., et al.: ImageNet classification with deep convolutional neural networks. Neural Information Processing Systems, 25, 1097\u22121105 (2012). https:\/\/doi.org\/10.1145\/3065386","DOI":"10.1145\/3065386"},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, pp. 248\u2013255, IEEE (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"17_CR13","unstructured":"LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010)"},{"issue":"5","key":"17_CR14","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1109\/JBHI.2018.2791863","volume":"22","author":"M Liu","year":"2018","unstructured":"Liu, M., et al.: Anatomical landmark based deep feature representation for MR images in brain disease diagnosis. IEEE J. Biomed. Health Inform. 22(5), 1476\u20131485 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Korolev, S., et al.: Residual and plain convolutional neural networks for 3D brain MRI classification. In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 835\u2013838. IEEE (2017)","DOI":"10.1109\/ISBI.2017.7950647"},{"key":"17_CR16","doi-asserted-by":"publisher","unstructured":"Xia, Z., et al.: A novel end-to-end hybrid network for Alzheimer\u2019s disease detection using 3D CNN and 3D CLSTM. In: IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20134. IEEE (2020). https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098621","DOI":"10.1109\/ISBI45749.2020.9098621"},{"key":"17_CR17","doi-asserted-by":"publisher","first-page":"63605","DOI":"10.1109\/ACCESS.2019.2913847","volume":"7","author":"C Feng","year":"2019","unstructured":"Feng, C., et al.: Deep learning framework for Alzheimer\u2019s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access 7, 63605\u201363618 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2913847","journal-title":"IEEE Access"},{"key":"17_CR18","doi-asserted-by":"publisher","first-page":"18150","DOI":"10.1038\/s41598-019-54548-6","volume":"9","author":"K Oh","year":"2019","unstructured":"Oh, K., et al.: Classification and visualization of Alzheimer\u2019s disease using volumetric convolutional neural network and transfer learning. Sci. Rep. 9, 18150 (2019). https:\/\/doi.org\/10.1038\/s41598-019-54548-6","journal-title":"Sci. Rep."},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Xing, X., et al.: Dynamic Image for 3D MRI Image Alzheimer\u2019s Disease Classification. ArXiv, abs\/2012.00119 (2020)","DOI":"10.1007\/978-3-030-66415-2_23"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Hon, M., et al.: Towards Alzheimer\u2019s disease classification through transfer learning. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1166\u20131169. IEEE (2017)","DOI":"10.1109\/BIBM.2017.8217822"},{"issue":"11","key":"17_CR21","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 (Basel, Switzerland) 19(11), 2645 (2019). https:\/\/doi.org\/10.3390\/s19112645","journal-title":"Sensors (Basel, Switzerland)"},{"key":"17_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/978-3-030-05587-5_34","volume-title":"Brain Informatics","author":"J Islam","year":"2018","unstructured":"Islam, J., Zhang, Y.: Deep convolutional neural networks for automated diagnosis of Alzheimer\u2019s disease and mild cognitive impairment using 3D Brain MRI. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 359\u2013369. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-05587-5_34"},{"key":"17_CR23","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1109\/LSP.2020.2964161","volume":"27","author":"JY Choi","year":"2020","unstructured":"Choi, J.Y., Lee, B.: Combining of multiple deep networks via ensemble generalization loss, based on MRI images, for Alzheimer\u2019s disease classification. IEEE Signal Process. Lett. 27, 206\u2013210 (2020). https:\/\/doi.org\/10.1109\/LSP.2020.2964161","journal-title":"IEEE Signal Process. Lett."},{"issue":"12","key":"17_CR24","doi-asserted-by":"publisher","first-page":"e05652","DOI":"10.1016\/j.heliyon.2020.e05652","volume":"6","author":"A Karim","year":"2020","unstructured":"Karim, A., et al.: Improving Alzheimer\u2019s stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities. Heliyon 6(12), e05652 (2020). https:\/\/doi.org\/10.1016\/j.heliyon.2020.e05652","journal-title":"Heliyon"},{"key":"17_CR25","doi-asserted-by":"publisher","unstructured":"Ebrahimi-Ghahnavieh, A., Luo, S., Chiong, R.: Transfer Learning for Alzheimer\u2019s Disease Detection on MRI Images. In: IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), pp. 133\u2013138. IEEE, (2019). https:\/\/doi.org\/10.1109\/ICIAICT.2019.8784845","DOI":"10.1109\/ICIAICT.2019.8784845"},{"key":"17_CR26","doi-asserted-by":"publisher","first-page":"80893","DOI":"10.1109\/ACCESS.2019.2919385","volume":"7","author":"X Hong","year":"2019","unstructured":"Hong, X., et al.: Predicting Alzheimer\u2019s Disease Using LSTM. IEEE Access 7, 80893\u201380901 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2919385","journal-title":"IEEE Access"},{"key":"17_CR27","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s12021-018-9370-4","volume":"16","author":"M Liu","year":"2018","unstructured":"Liu, M., et al.: Multimodality cascaded convolutional neural networks for Alzheimer\u2019s disease diagnosis. Neuroinformatics 16, 295\u2013308 (2018)","journal-title":"Neuroinformatics"},{"key":"17_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/978-3-319-68600-4_43","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2017","author":"S Wang","year":"2017","unstructured":"Wang, S., Shen, Y., Chen, W., Xiao, T., Hu, J.: Automatic recognition of mild cognitive impairment from MRI images using expedited convolutional neural networks. In: Lintas, A., Rovetta, S., Verschure, P.F.M.J., Villa, A.E.P. (eds.) ICANN 2017. LNCS, vol. 10613, pp. 373\u2013380. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68600-4_43"},{"key":"17_CR29","doi-asserted-by":"publisher","first-page":"259","DOI":"10.3389\/fnins.2020.00259","volume":"14","author":"D Pan","year":"2020","unstructured":"Pan, D., et al.: Early detection of Alzheimer\u2019s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Front. Neurosci. 14, 259 (2020)","journal-title":"Front. Neurosci."},{"key":"17_CR30","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.neuroimage.2014.06.077","volume":"101","author":"HI Suk","year":"2014","unstructured":"Suk, H.I., et al.: Hierarchical feature representation and multimodal fusion with deep learning for AD\/MCI diagnosis. Neuroimage 101, 569\u2013582 (2014)","journal-title":"Neuroimage"},{"issue":"3","key":"17_CR31","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379\u2013423 (1948)","journal-title":"Bell Syst. Tech. J."},{"key":"17_CR32","unstructured":"Kaggle: Your Home for Data Science. https:\/\/www.kaggle.com\/. Accessed 08 Jul 2021"}],"container-title":["Lecture Notes in Computer Science","Distributed Computing and Intelligent Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-94876-4_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T10:23:38Z","timestamp":1674469418000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-94876-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030948757","9783030948764"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-94876-4_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"17 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDCIT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Distributed Computing and Internet Technology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bhubaneswar","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 January 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 January 2022","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":"icdcit2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icdcit.ac.in\/","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":"50","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":"11","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":"4","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":"22% - 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":"3.2","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":"2.7","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Additionally, 4 invited papers are included.","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)"}}]}}