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Title: | COVID-19 image classification using deep learning: Advances, challenges and opportunities |
Other Titles: | Electronics Communication / Telecommunication |
Authors: | Binish Fatimah |
Keywords: | Electronics Communication / Telecommunication |
Issue Date: | Jun-2022 |
Publisher: | ScienceDirect |
Abstract: | Corona Virus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS- CoV-2), is a highly contagious disease that has affected the lives of millions around the world. Chest X-Ray (CXR) and Computed Tomography (CT) imaging modalities are widely used to obtain a fast and accurate diagnosis of COVID-19. However, manual identification of the infection through radio images is extremely challenging because it is time-consuming and highly prone to human errors. Artificial Intelligence (AI)-techniques have shown potential and are being exploited further in the development of automated and accurate solutions for COVID-19 detection. Among AI methodologies, Deep Learning (DL) algorithms, particularly Convolutional Neural Networks (CNN), have gained significant popularity for the classification of COVID-19. This paper summarizes and reviews a number of significant research publications on the DL-based classification of COVID- 19 through CXR and CT images. We also present an outline of the current state-of-the-art advances and a critical discussion of open challenges. We conclude our study by enumerating some future directions of research in COVID-19 imaging classification. |
URI: | http://localhost:8080/xmlui/handle/123456789/6662 |
Appears in Collections: | 2022 |
Files in This Item:
File | Description | Size | Format | |
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4. PAPER4ECE.pdf | 249.19 kB | Adobe PDF | View/Open |
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