Covid-19 Imaging Tools: How Big Data is Big?
J Med Syst
; 45(7): 71, 2021 Jun 03.
Article
in English
| MEDLINE | ID: covidwho-1252169
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Radiography, Thoracic
/
Tomography, X-Ray Computed
/
Big Data
/
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Med Syst
Year:
2021
Document Type:
Article
Affiliation country:
S10916-021-01747-2
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