A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction.
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| Title: | A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction. |
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| Authors: | Ahmadi M; Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States of America., Javaheri D; Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea., Khajavi M; Foster School of Businesses, University of Washington, Seattle, Washington, United States of America., Danesh K; Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States of America., Hur J; Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea. |
| Source: | PloS one [PLoS One] 2024 Mar 26; Vol. 19 (3), pp. e0297996. Date of Electronic Publication: 2024 Mar 26 (Print Publication: 2024). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0297996 |
