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Benchmarking of Shallow Learning and Deep Learning Techniques with Transfer Learning for Neurodegenerative Disease Assessment Through Handwriting

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Document Analysis and Recognition – ICDAR 2021 Workshops (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12917))

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Abstract

Neurodegenerative diseases are incurable diseases where a timely diagnosis plays a key role. For this reason, various techniques of computer aided diagnosis (CAD) have been proposed. In particular handwriting is a well-established diagnosis technique. For this reason, an analysis of state-of-the-art technologies, compared to those which historically proved to be effective for diagnosis, remains of primary importance. In this paper a benchmark between shallow learning techniques and deep neural network techniques with transfer learning are provided: their performance is compared to that of classical methods in order to quantitatively estimate the possibility of performing advanced assessment of neurodegenerative disease through both offline and online handwriting. Moreover, a further analysis of their performance on the subset of a new dataset, which makes use of standardized handwriting tasks, is provided to determine the impact of the various benchmarked techniques and draw new research directions.

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Authors and Affiliations

  1. University of Bari “Aldo Moro”, Via Orabona 4, Bari, Italy

    Vincenzo Dentamaro, Paolo Giglio, Donato Impedovo & Giuseppe Pirlo

Authors
  1. Vincenzo Dentamaro
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  2. Paolo Giglio
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  3. Donato Impedovo
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  4. Giuseppe Pirlo
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Corresponding author

Correspondence to Donato Impedovo .

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Editors and Affiliations

  1. Boise State University, Boise, ID, USA

    Elisa H. Barney Smith

  2. Indian Statistical Institute, Kolkata, India

    Umapada Pal

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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The authors declare no conflict of interest.

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Dentamaro, V., Giglio, P., Impedovo, D., Pirlo, G. (2021). Benchmarking of Shallow Learning and Deep Learning Techniques with Transfer Learning for Neurodegenerative Disease Assessment Through Handwriting. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12917. Springer, Cham. https://doi.org/10.1007/978-3-030-86159-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-86159-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86158-2

  • Online ISBN: 978-3-030-86159-9

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