1. Covid-19 Imaging Tools: How Big Data is Big?
- Author
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Santosh, KC and Ghosh, Sourodip
- Subjects
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DIGITAL image processing , *DEEP learning , *VIRAL pneumonia , *COVID-19 , *CHEST X rays , *ARTIFICIAL intelligence , *MEDICAL screening , *COMPUTED tomography , *SOFTWARE analytics , *ARTIFICIAL neural networks , *ALGORITHMS - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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