Abstract
Deep learning is a prominent method for automatic detection of COVID-19 disease using a medical dataset. This paper aims to give a perspective on the data insufficiency issue that exists in COVID-19 detection associated with deep learning. The extensive study of the available datasets comprising CT and X-ray images is presented in this paper, which can be very much useful in the context of a deep learning framework for COVID-19 detection. Moreover, various data handling techniques that are very essential in deep learning models are discussed in detail. Advanced data handling techniques and approaches to modify deep learning models are suggested to handle the data insufficiency problem in deep learning based on COVID-19 detection.
Keywords: COVID-19, CT dataset, chest X-ray dataset, deep learning, data augmentation, transfer learning.
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