Diagnosis of COVID-19 from X-ray images using deep learning techniques. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Alghamdi, Maha Mesfer Meshref; Dahab, Mohammed Yehia Hassan
• Is Part Of: Cogent engineering. Volume 9:Issue 1(2022)
• Publication Date: 2022-12-31
• Subjects: deep learning -- CXR imaging -- convolutional neural networks -- COVID-19 -- machine learning -- artificial intelligence; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2022.2124635
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: In this study, we searched for the latest literature on the use of deep learning applications to combat COVID-19 and these were identified from several search engines including IEEE Xplore, Google Scholar, PubMed, and Scopus. This involved a comprehensive analysis of the studies to identify the challenges associated with the use of deep learning models with a view to highlight the possible future trends in the development of deep learning systems that are efficient and more reliable for the diagnosis of COVID-19 patients. This paper provides information related to the deep learning techniques used to detect COVID-19. This paper discusses the Convolutional Neural Networks' (CNNs) structure, how to train CNNs, and highlights the different pre-trained models of CNNs that can be used for the detection of COVID-19. This paper explores the latest developments in the diagnosis of COVID-19 using deep learning applications that rely on the use of X-ray images taken from medical imaging samples. A review of the different models developed to facilitate effective diagnosis of COVID-19 provides information regarding the experimental data, the data splitting techniques used, as well as the proposed architecture for detecting COVID-19 and the different evaluation metrics for each model. This paper is a useful resource for medical and technical experts, as it helps them to develop a sound understanding of how deep learning techniques can be harnessed to stop the spread of COVID-19.