{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T18:22:25Z","timestamp":1792002145368,"version":"4.4.0"},"reference-count":28,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chosun University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the last three decades, the development of functional magnetic resonance imaging (fMRI) has significantly contributed to the understanding of the brain, functional brain mapping, and resting-state brain networks. Given the recent successes of deep learning in various fields, we propose a 3D-CNN-LSTM classification model to diagnose health conditions with the following classes: condition normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer\u2019s disease (AD). The proposed method employs spatial and temporal feature extractors, wherein the former utilizes a U-Net architecture to extract spatial features, and the latter utilizes long short-term memory (LSTM) to extract temporal features. Prior to feature extraction, we performed four-step pre-processing to remove noise from the fMRI data. In the comparative experiments, we trained each of the three models by adjusting the time dimension. The network exhibited an average accuracy of 96.4% when using five-fold cross-validation. These results show that the proposed method has high potential for identifying the progression of Alzheimer\u2019s by analyzing 4D fMRI data.<\/jats:p>","DOI":"10.3390\/s23146330","type":"journal-article","created":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T01:58:11Z","timestamp":1689213491000},"page":"6330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Classification of Alzheimer\u2019s Progression Using fMRI Data"],"prefix":"10.3390","volume":"23","author":[{"given":"Ju-Hyeon","family":"Noh","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, University of Chosun, Gwangju 61452, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun-Hyeok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Chosun, Gwangju 61452, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7726-1442","authenticated-orcid":false,"given":"Hee-Deok","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of Chosun, Gwangju 61452, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1093\/oxfordjournals.aje.a009489","article-title":"Incidence and Risk of Dementia: The Rotterdam Study","volume":"147","author":"Ott","year":"1998","journal-title":"Am. 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