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Development of an Interpretable Deep Learning System for the Identification of Patients with Alzheimer’s Disease

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Advancement of Science and Technology in Sustainable Manufacturing and Process Engineering

Part of the book series: Green Energy and Technology ((GREEN))

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Abstract

Deep learning using convolutional neural networks has shown great promise in analyzing neuroimaging data. Identification of Alzheimer’s disease patients from healthy individuals using structural magnetic resonance data is one of the clinical problems that has been widely explored by employing convolutional neural networks and producing very high classification accuracies. However, in most studies, the results were not supported by explainability tools.

In this study, an interpretable convolutional neural network model derived from pre-trained VGG-16 is proposed to classify Alzheimer’s disease patients versus healthy subjects using MRI data obtained from an Open Access Series of Imaging Studies (OASIS) dataset. The model was trained and validated based on a five-fold cross-validation loop and produced a classification accuracy of 71.62% on the validation set. Moreover, we incorporated four CNN visualization techniques that highlight important brain regions used by the model to identify AD patients: saliency map, gradient class activation map**, occlusion map**, and heatmap generated by Shapley additive explanation (SHAP) method. The potential of these explainability tools in identifying biased models that produce inflated erroneous results is also investigated, and the resulting heatmaps were able to indicate a bias in the model’s training procedure.

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Author information

Authors and Affiliations

  1. Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy

    Selamawet Workalemahu Atnafu & Stefano Diciotti

  2. Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia

    Selamawet Workalemahu Atnafu

Authors
  1. Selamawet Workalemahu Atnafu
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  2. Stefano Diciotti
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Editor information

Editors and Affiliations

  1. Chemical and Biochemical Engineering, Western University, London, ON, Canada

    Kibret Mequanint

  2. Mechanical Engineering, Bahir Dar University, Bahir Dar, Ethiopia

    Assefa Asmare Tsegaw

  3. Chemical Engineering, Bahir Dar University, Bahir Dar, Ethiopia

    Zenamarkos Bantie Sendekie

  4. Civil and Water Resources Engineering, Bahir Dar University, Bahir Dar, Ethiopia

    Birhanu Kebede

  5. Civil and Water Resources Engineering, Bahir Dar University, Bahir Dar, Ethiopia

    Ephrem Yetbarek Gedilu

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Atnafu, S.W., Diciotti, S. (2024). Development of an Interpretable Deep Learning System for the Identification of Patients with Alzheimer’s Disease. In: Mequanint, K., Tsegaw, A.A., Sendekie, Z.B., Kebede, B., Yetbarek Gedilu, E. (eds) Advancement of Science and Technology in Sustainable Manufacturing and Process Engineering. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-41173-1_2

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  • DOI: https://doi.org/10.1007/978-3-031-41173-1_2

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  • Online ISBN: 978-3-031-41173-1

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