{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T01:10:51Z","timestamp":1774746651701,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,6]],"date-time":"2023-08-06T00:00:00Z","timestamp":1691280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Alzheimer\u2019s disease is a chronic neurodegenerative disease that causes brain cells to degenerate, resulting in decreased physical and mental abilities and, in severe cases, permanent memory loss. It is considered as the most common and fatal form of dementia. Although mild cognitive impairment (MCI) precedes Alzheimer\u2019s disease (AD), it does not necessarily show the obvious symptoms of AD. As a result, it becomes challenging to distinguish between mild cognitive impairment and cognitively normal. In this paper, we propose an ensemble of deep learners based on convolutional neural networks for the early diagnosis of Alzheimer\u2019s disease. The proposed approach utilises simple averaging ensemble and weighted averaging ensemble methods. The ensemble-based transfer learning model demonstrates enhanced generalization and performance for AD diagnosis compared to traditional transfer learning methods. Extensive experiments on the OASIS-3 dataset validate the effectiveness of the proposed model, showcasing its superiority over state-of-the-art transfer learning approaches in terms of accuracy, robustness, and efficiency.<\/jats:p>","DOI":"10.3390\/a16080377","type":"journal-article","created":{"date-parts":[[2023,8,6]],"date-time":"2023-08-06T10:01:53Z","timestamp":1691316113000},"page":"377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Ensemble Transfer Learning for Distinguishing Cognitively Normal and Mild Cognitive Impairment Patients Using MRI"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4473-5628","authenticated-orcid":false,"given":"Pratham","family":"Grover","sequence":"first","affiliation":[{"name":"Department of Biotechnology, Delhi Technological University, Bawana Road, Delhi 110042, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunal","family":"Chaturvedi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4265-2205","authenticated-orcid":false,"given":"Xing","family":"Zi","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Saxena","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Guru Ghasidas University, Bilaspur 495009, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7610-3004","authenticated-orcid":false,"given":"Shiv","family":"Prakash","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication, University of Allahabad, Allahabad 211002, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3114-8978","authenticated-orcid":false,"given":"Tony","family":"Jan","sequence":"additional","affiliation":[{"name":"School of Information Technology, Torrens University, Sydney 2010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7745-9667","authenticated-orcid":false,"given":"Mukesh","family":"Prasad","sequence":"additional","affiliation":[{"name":"School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2022). 2022 Alzheimer\u2019s Disease Facts and Figures. 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