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04.06.2021 | COVID-19 | Education & Training Zur Zeit gratis

Covid-19 Imaging Tools: How Big Data is Big?

verfasst von: KC Santosh, Sourodip Ghosh

Erschienen in: Journal of Medical Systems | Ausgabe 7/2021

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Abstract

In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation. Medical imaging tools do not explicitly analyze model fitting. Also, using transfer learning, with fewer data, one could possibly build Covid-19 deep learning model but they are limited to education and training. We observe that, in both image modalities, neither the dataset size nor does data augmentation work well for Covid-19 screening purposes because a large dataset does not guarantee all possible Covid-19 manifestations and data augmentation does not create new Covid-19 cases.
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Metadaten
Titel
Covid-19 Imaging Tools: How Big Data is Big?
verfasst von
KC Santosh
Sourodip Ghosh
Publikationsdatum
04.06.2021
Verlag
Springer US
Schlagwort
COVID-19
Erschienen in
Journal of Medical Systems / Ausgabe 7/2021
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-021-01747-2

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