A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer’s severity using multitask feature extraction

• Mohsen Ahmadi,

  Roles Formal analysis, Investigation, Writing – original draft

  Affiliation Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States of America

  https://orcid.org/0000-0003-1550-110X

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• Danial Javaheri ,

  Roles Conceptualization, Formal analysis, Supervision, Validation, Visualization, Writing – review & editing

  * E-mail: jbhur@korea.ac.kr (JH); javaheri@korea.ac.kr (DJ)

  Affiliation Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea

  https://orcid.org/0000-0002-7275-2370

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• Matin Khajavi,

  Roles Investigation, Validation, Writing – review & editing

  Affiliation Foster School of Businesses, University of Washington, Seattle, Washington, United States of America

  https://orcid.org/0009-0004-7883-8812

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• Kasra Danesh,

  Roles Conceptualization, Investigation, Validation, Writing – review & editing

  Affiliation Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States of America

  https://orcid.org/0009-0006-7706-0142

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• Junbeom Hur

  Roles Conceptualization, Formal analysis, Funding acquisition, Supervision, Validation, Visualization, Writing – review & editing

  * E-mail: jbhur@korea.ac.kr (JH); javaheri@korea.ac.kr (DJ)

  Affiliation Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea

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