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.

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

Dentamaro, V
;
Giglio, P
;
Impedovo, D;Pirlo, G
2021-01-01

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.
2021
978-3-030-86158-2
978-3-030-86159-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/410841
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