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

Autor:	Giuseppe Pirlo, Donato Impedovo, Paolo Giglio, Vincenzo Dentamaro
Rok vydání:	2021
Předmět:	Artificial neural network Computer science business.industry Deep learning CAD Benchmarking Machine learning computer.software_genre Computer-aided diagnosis Handwriting Benchmark (computing) Artificial intelligence Transfer of learning business computer
Zdroj:	Document Analysis and Recognition – ICDAR 2021 Workshops ISBN: 9783030861582 ICDAR Workshops (2)
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::10907f2ed79a2d3758764678c8a9cbb5 https://doi.org/10.1007/978-3-030-86159-9_1 Zobrazit plný text záznamu