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Exploring Deep Transfer Learning Ensemble for Improved Diagnosis and Classification of Alzheimer’s Disease

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Brain Informatics (BI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13974))

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Abstract

Alzheimer’s disease (AD) is a progressive and irreversible neurological disorder that affects millions of people worldwide. Early detection and accurate diagnosis of AD are crucial for effective treatment and management of the disease. In this paper, we propose a transfer learning-based approach for the diagnosis of AD using magnetic resonance imaging (MRI) data. Our approach involves extracting relevant features from the MRI data using transfer learning by alter the weights and then using these features to train pre-trained models and combined ensemble classifier. We evaluated our approach on a dataset of MRI scans from patients with AD and healthy controls, achieving an accuracy of 95% for combined ensemble models. Our results demonstrate the potential of transfer learning-based approaches for the early and accurate diagnosis of AD, which could lead to improved patient outcomes and more effective management of the disease.

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Author information

Authors and Affiliations

  1. Rangamati Science and Technology University, 4500, Rangamati, Bangladesh

    Tanjim Mahmud, Koushick Barua, Anik Barua & Sudhakar Das

  2. Leeds Beckette University, Leeds, UK

    Nanziba Basnin

  3. University of Chittagong, Chittagong, Bangladesh

    Mohammad Shahadat Hossain

  4. Pervasive and Mobile Computing Laboratory, Lulea University of Technology, S-931 87, Skelleftea, Sweden

    Karl Andersson

  5. Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  6. Chattogram Maa-O-Shishu Hospital Medical College, Chittagong, Bangladesh

    Nahed Sharmen

Authors
  1. Tanjim Mahmud
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  2. Koushick Barua
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  4. Sudhakar Das
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  5. Nanziba Basnin
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  6. Mohammad Shahadat Hossain
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  7. Karl Andersson
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  8. M. Shamim Kaiser
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Correspondence to Tanjim Mahmud .

Editor information

Editors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Feng Liu

  2. Lehigh University, Bethlehem, PA, USA

    Yu Zhang

  3. Maebashi Institute of Technology, Maebashi, Japan

    Hongzhi Kuai

  4. Boston University, Boston, MA, USA

    Emily P. Stephen

  5. Stevens Institute of Technology, Hoboken, NJ, USA

    Hongjun Wang

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Mahmud, T. et al. (2023). Exploring Deep Transfer Learning Ensemble for Improved Diagnosis and Classification of Alzheimer’s Disease. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_10

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  • DOI: https://doi.org/10.1007/978-3-031-43075-6_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-43075-6

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