{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:58:46Z","timestamp":1787673526465,"version":"build-2736575974"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"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":["Multimedia Systems"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1007\/s00530-021-00797-3","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:01:36Z","timestamp":1620086496000},"page":"85-94","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":122,"title":["Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5665-4615","authenticated-orcid":false,"given":"Saeeda","family":"Naz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abida","family":"Ashraf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Zaib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"issue":"12","key":"797_CR1","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1212\/WNL.0000000000000240","volume":"82","author":"BD James","year":"2014","unstructured":"James, B.D., Leurgans, S.E., Hebert, L.E., Scherr, P.A., Yaffe, K., Bennett, D.A.: Contribution of Alzheimer disease to mortality in the united states. Neurology 82(12), 1045\u20131050 (2014)","journal-title":"Neurology"},{"issue":"3","key":"797_CR2","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.jalz.2007.04.381","volume":"3","author":"R Brookmeyer","year":"2007","unstructured":"Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer\u2019s disease. Alzheimer\u2019s Dement. 3(3), 186\u2013191 (2007)","journal-title":"Alzheimer\u2019s Dement."},{"key":"797_CR3","doi-asserted-by":"crossref","unstructured":"Thakare P., Pawar V.: Alzheimer disease detection and tracking of Alzheimer patient. In: 2016 International Conference on Inventive Computation Technologies (ICICT); vol.\u00a01. IEEE; 2016, p. 1\u20134","DOI":"10.1109\/INVENTIVE.2016.7823286"},{"key":"797_CR4","doi-asserted-by":"publisher","DOI":"10.5772\/48558","volume-title":"Complementary Therapies for the Contemporary Healthcare, chap.\u00a010","author":"B Abdalla","year":"2012","unstructured":"Abdalla, B., Yassin, M., Abir, M., Bisharat, B., Armaly, Z.: Traditional and modern medicine harmonizing the two approaches in the treatment of neurodegeneration (Alzheimer\u2019s disease - ad). In: Saad, M., de Medeiros, R. (eds.) Complementary Therapies for the Contemporary Healthcare, chap.\u00a010. IntechOpen, Rijeka (2012). https:\/\/doi.org\/10.5772\/48558"},{"issue":"14","key":"797_CR5","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1001\/jama.2015.2852","volume":"313","author":"J Jin","year":"2015","unstructured":"Jin, J.: Alzheimer disease. JAMA 313(14), 1488\u20131488 (2015)","journal-title":"JAMA"},{"issue":"2","key":"797_CR6","doi-asserted-by":"publisher","first-page":"023002","DOI":"10.1117\/1.JEI.29.2.023002","volume":"29","author":"S Zahoor","year":"2020","unstructured":"Zahoor, S., Naz, S., Khan, N.H., Razzak, M.I.: Deep optical character recognition: a case of Pashto language. J. Electron. Imaging 29(2), 023002 (2020)","journal-title":"J. Electron. Imaging"},{"issue":"5","key":"797_CR7","doi-asserted-by":"publisher","first-page":"e12565","DOI":"10.1111\/exsy.12565","volume":"37","author":"S Naz","year":"2020","unstructured":"Naz, S., Khan, N.H., Zahoor, S., Razzak, M.I.: Deep OCR for Arabic script-based language like Pastho. Expert Syst. 37(5), e12565 (2020)","journal-title":"Expert Syst."},{"key":"797_CR8","doi-asserted-by":"publisher","first-page":"17149","DOI":"10.1109\/ACCESS.2018.2890810","volume":"7","author":"A Rehman","year":"2019","unstructured":"Rehman, A., Naz, S., Razzak, M.I., Hameed, I.A.: Automatic visual features for writer identification: a deep learning approach. IEEE Access 7, 17149\u201317157 (2019)","journal-title":"IEEE Access"},{"issue":"8","key":"797_CR9","first-page":"219","volume":"26","author":"S Naz","year":"2015","unstructured":"Naz, S., Umar, A.I., Ahmad, R., Ahmed, S.B., Shirazi, S.H., et al.: Urdu Nasta\u2019liq text recognition system based on multi-dimensional recurrent neural network and statistical features. Neural Comput. Appl. 26(8), 219\u2013231 (2015)","journal-title":"Neural Comput. Appl."},{"key":"797_CR10","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neucom.2015.11.030","volume":"177","author":"S Naz","year":"2016","unstructured":"Naz, S., Umar, A.I., Ahmed, R.A.S.B., Siddiqi, I., Razzak, M.I.: Offline cursive Nastaliq script recognition using multidimensional recurrent neural networks with statistical features. Neurocomputing 177, 228\u2013241 (2016)","journal-title":"Neurocomputing"},{"issue":"3","key":"797_CR11","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/s00521-019-04069-0","volume":"32","author":"A Naseer","year":"2020","unstructured":"Naseer, A., Rani, M., Naz, S., Razzak, M.I., Imran, M., Xu, G.: Refining Parkinson\u2019s neurological disorder identification through deep transfer learning. Neural Comput. Appl. 32(3), 839\u2013854 (2020)","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"797_CR12","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s00034-019-01246-3","volume":"39","author":"A Rehman","year":"2020","unstructured":"Rehman, A., Naz, S., Razzak, M.I., Akram, F., Imran, M.: A deep learning-based framework for automatic brain tumors classification using transfer learning. Circ. Syst. Signal Process. 39(2), 757\u2013775 (2020)","journal-title":"Circ. Syst. Signal Process."},{"key":"797_CR13","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., Lee, S.W., Shen, D., Initiative, A.D.N., et al.: Hierarchical feature representation and multimodal fusion with deep learning for ad\/mci diagnosis. NeuroImage 101, 569\u2013582 (2014)","journal-title":"NeuroImage"},{"key":"797_CR14","doi-asserted-by":"crossref","unstructured":"Sarraf S., Tofighi G., et\u00a0al. Deepad: Alzheimer s disease classification via deep convolutional neural networks using mri and fmri. BioRxiv 070441 (2016)","DOI":"10.1101\/070441"},{"key":"797_CR15","doi-asserted-by":"crossref","unstructured":"Mathew, J., Mekkayil, L., Ramasangu, H., Karthikeyan, B.R., Manjunath, A.G.: Robust algorithm for early detection of alzheimer\u2019s disease using multiple feature extractions. In: IEEE Annual India Conference (INDICON). IEEE 2016, 1\u20136 (2016)","DOI":"10.1109\/INDICON.2016.7839026"},{"key":"797_CR16","doi-asserted-by":"crossref","unstructured":"Iftikhar M.A., Idris A.: An ensemble classification approach for automated diagnosis of Alzheimer\u2019s disease and mild cognitive impairment. In: 2016 International Conference on Open Source Systems & Technologies (ICOSST). IEEE; p. 78\u201383 (2016)","DOI":"10.1109\/ICOSST.2016.7838581"},{"key":"797_CR17","doi-asserted-by":"crossref","unstructured":"Hosseini-Asl E., Keynton R., El-Baz A.: Alzheimer\u2019s disease diagnostics by adaptation of 3d convolutional network. In: 2016 IEEE International Conference on Image Processing (ICIP). IEEE; p. 126\u2013130 (2016)","DOI":"10.1109\/ICIP.2016.7532332"},{"issue":"1","key":"797_CR18","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/TCBB.2017.2776910","volume":"16","author":"R Ju","year":"2019","unstructured":"Ju, R., Hu, C., Zhou, P., Li, Q.: Early diagnosis of Alzheimer\u2019s disease based on resting-state brain networks and deep learning. IEEE\/ACM Trans. Comput. Biol. Bioinform. (TCBB) 16(1), 244\u2013257 (2019)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform. (TCBB)"},{"key":"797_CR19","doi-asserted-by":"crossref","unstructured":"Farooq A., Anwar S., Awais M., Rehman S.: A deep cnn based multi-class classification of Alzheimer\u2019s disease using MRI. In: 2017 IEEE International Conference on Imaging systems and techniques (IST). IEEE; p. 1\u20136 (2017)","DOI":"10.1109\/IST.2017.8261460"},{"key":"797_CR20","doi-asserted-by":"crossref","unstructured":"B\u00e4ckstr\u00f6m, K., Nazari, M., Gu, I.Y.H., Jakola, A.S.: An efficient 3d deep convolutional network for Alzheimer\u2019s disease diagnosis using MR images. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE 2018, 149\u2013153 (2018)","DOI":"10.1109\/ISBI.2018.8363543"},{"key":"797_CR21","doi-asserted-by":"crossref","unstructured":"Kazemi Y., Houghten S.: A deep learning pipeline to classify different stages of Alzheimer\u2019s disease from FMRI data. In: 2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE; 2018, p. 1\u20138 (2018)","DOI":"10.1109\/CIBCB.2018.8404980"},{"key":"797_CR22","first-page":"737","volume":"10","author":"S Qiu","year":"2018","unstructured":"Qiu, S., Chang, G.H., Panagia, M., Gopal, D.M., Au, R., Kolachalama, V.B.: Fusion of deep learning models of MRI scans, mini-mental state examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit. 10, 737\u2013749 (2018)","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"797_CR23","doi-asserted-by":"publisher","first-page":"777","DOI":"10.3389\/fnins.2018.00777","volume":"12","author":"W Lin","year":"2018","unstructured":"Lin, W., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., et al.: Convolutional neural networks-based MRI image analysis for the Alzheimer\u2019 disease prediction from mild cognitive impairment. Front. Neurosci. 12, 777 (2018)","journal-title":"Front. Neurosci."},{"key":"797_CR24","unstructured":"Payan A., Montana G.: Predicting Alzheimer\u2019s disease: a neuroimaging study with 3d convolutional neural networks. arXiv preprint arXiv:150202506 (2015)"},{"key":"797_CR25","doi-asserted-by":"crossref","unstructured":"Xia, Z., Yue, G., Xu, Y., Feng, C., Yang, M., Wang, T.: A novel end-to-end hybrid network for Alzheimer\u2019 disease detection using 3d CNN and 3d CLSTM. In: IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE 2020, 1\u20134 (2020)","DOI":"10.1109\/ISBI45749.2020.9098621"},{"key":"797_CR26","doi-asserted-by":"crossref","unstructured":"Ebrahimi-Ghahnavieh A., Luo S., Chiong R.: Transfer learning for Alzheimer\u2019s disease detection on MRI images. In: 2019 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE; 2019, p. 133\u2013138 (2019)","DOI":"10.1109\/ICIAICT.2019.8784845"},{"key":"797_CR27","doi-asserted-by":"crossref","unstructured":"Ashraf, A., Naz, S., Shirazi, S.H., Razzak, I., Parsad, M.: Deep transfer learning for Alzheimer neurological disorder detection. Multimed. Tools Appl., 1\u201326 (2021)","DOI":"10.1007\/s11042-020-10331-8"},{"key":"797_CR28","doi-asserted-by":"crossref","unstructured":"Mehmood, A., Ahmad, A.S., Maqsood, M., Yaqub, M., et al.: A transfer learning approach for early diagnosis of Alzheimer\u2019 disease on MRI images. Neuroscience. 460, 43\u201352 (2021)","DOI":"10.1016\/j.neuroscience.2021.01.002"},{"key":"797_CR29","doi-asserted-by":"publisher","first-page":"106032","DOI":"10.1016\/j.cmpb.2021.106032","volume":"203","author":"J Liu","year":"2021","unstructured":"Liu, J., Li, M., Luo, Y., Yang, S., Li, W., Bi, Y.: Alzheimer\u2019s disease detection using depthwise separable convolutional neural networks. Comput. Methods Programs Biomed. 203, 106032 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"797_CR30","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xia, Y.: Iterative sparse and deep learning for accurate diagnosis of Alzheimer\u2019s disease. Pattern Recognit., 107944 (2021)","DOI":"10.1016\/j.patcog.2021.107944"},{"issue":"4","key":"797_CR31","first-page":"38","volume":"6","author":"C Sandeep","year":"2017","unstructured":"Sandeep, C., Kumar, A.S., Susanth, M.: The online datasets used to classify the different stages for the early diagnosis of Alzheimer\u2019 disease (ad). Int. J. Eng. Adv. Technol. 6(4), 38\u201345 (2017)","journal-title":"Int. J. Eng. Adv. Technol."},{"issue":"4","key":"797_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3404374","volume":"16","author":"C Yan","year":"2020","unstructured":"Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 16(4), 1\u201317 (2020)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"issue":"11","key":"797_CR33","doi-asserted-by":"publisher","first-page":"3014","DOI":"10.1109\/TMM.2020.2967645","volume":"22","author":"C Yan","year":"2020","unstructured":"Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Xu, F.: 3d room layout estimation from a single RGB image. IEEE Trans. Multimed. 22(11), 3014\u20133024 (2020b)","journal-title":"IEEE Trans. Multimed."},{"key":"797_CR34","doi-asserted-by":"crossref","unstructured":"Yan, C., Gong, B., Wei, Y., Gao, Y.: Deep multi-view enhancement hashing for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 43 (2020)","DOI":"10.1109\/TPAMI.2020.2975798"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00797-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-021-00797-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00797-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T01:07:55Z","timestamp":1643332075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-021-00797-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,4]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["797"],"URL":"https:\/\/doi.org\/10.1007\/s00530-021-00797-3","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,4]]},"assertion":[{"value":"27 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}