Treffer: Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.

Title:
Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.
Authors:
Gaur L; Amity International Business School, Amity University, Noida, India., Bhatia U; Amity International Business School, Amity University, Noida, India., Jhanjhi NZ; School of Computer Science and Engineering SCE, Taylor's University, Subang Jaya, Malaysia., Muhammad G; Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh 11543, Saudi Arabia.; Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia., Masud M; Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944 Saudi Arabia.
Source:
Multimedia systems [Multimed Syst] 2023; Vol. 29 (3), pp. 1729-1738. Date of Electronic Publication: 2021 Apr 28.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101710967 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0942-4962 (Print) Linking ISSN: 09424962 NLM ISO Abbreviation: Multimed Syst Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: Berlin : Springer-Verlag
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Contributed Indexing:
Keywords: COVID-19; Chest X-rays; Computer vision; Deep CNN; Deep learning; Transfer learning
Entry Date(s):
Date Created: 20210503 Latest Revision: 20231102
Update Code:
20250114
PubMed Central ID:
PMC8079233
DOI:
10.1007/s00530-021-00794-6
PMID:
33935377
Database:
MEDLINE

Weitere Informationen

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
(© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

Conflict of interestThe authors declare that they have no conflict of interest.