{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T14:46:31Z","timestamp":1789569991534,"version":"build-2803163510"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>One of the first signs of Alzheimer\u2019s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.<\/jats:p>","DOI":"10.3390\/diagnostics11061071","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T13:31:39Z","timestamp":1623331899000},"page":"1071","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":235,"title":["Analysis of Features of Alzheimer\u2019s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1082-185X","authenticated-orcid":false,"given":"Modupe","family":"Odusami","sequence":"first","affiliation":[{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2809-2213","authenticated-orcid":false,"given":"Rytis","family":"Maskeli\u016bnas","sequence":"additional","affiliation":[{"name":"Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8509-420X","authenticated-orcid":false,"given":"Tomas","family":"Krilavi\u010dius","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1148\/radiol.2018180958","article-title":"A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain","volume":"290","author":"Ding","year":"2019","journal-title":"Radiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.neucom.2019.04.093","article-title":"Multi-modal deep learning model for auxiliary diagnosis of Alzheimer\u2019s disease","volume":"361","author":"Zhang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101859","DOI":"10.1016\/j.nicl.2019.101859","article-title":"Diagnosis and prognosis of Alzheimer\u2019s disease using brain morphometry and white matter connectomes","volume":"23","author":"Wang","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1109\/JBHI.2018.2798062","article-title":"Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer\u2019s Disease","volume":"22","author":"Alberdi","year":"2018","journal-title":"IEEE J. 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