Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/6662
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 SizeFormat 
4. PAPER4ECE.pdf249.19 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.