Advancements in Automated Detection of COVID-19 in Human Chest CT Scans Using DLNN Techniques

The automated recognition of COVID-19 in human Chest CT scans has emerged as a vital tool in the fight against the global pandemic. This paper presents an outline of recent advancements in the automated recognition of COVID-19 in human chest CT scans through the application of deep learning neural n...

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Bibliographic Details
Published in:	Trends in biomaterials & artificial organs Vol. 38; no. 2; pp. 99 - 104
Main Authors:	Javhav, Aravind, Pujari, Sanjay
Format:	Journal Article
Language:	English
Published:	Society for Biomaterials and Artificial Organs 01-04-2024
Subjects:	Computational linguistics CT imaging Diagnostic imaging Information management Language processing Medical imaging equipment Methods Natural language interfaces Neural networks Technology application
Online Access:	Get full text
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Summary:	The automated recognition of COVID-19 in human Chest CT scans has emerged as a vital tool in the fight against the global pandemic. This paper presents an outline of recent advancements in the automated recognition of COVID-19 in human chest CT scans through the application of deep learning neural network techniques. Chest CT scans have been proven to be a valuable tool for identifying COVID-19-related abnormalities in the lungs, and the integration of DL models have significantly enhanced the efficiency and accuracy of this process. This paper also explores the evolving landscape of automated COVID-19 identification, highlighting the role of deep learning in transforming diagnostic capabilities. It discusses the challenges, including data quality and privacy concerns, as well as the promising solutions that have emerged. The data gram is enhanced by using DLNN, MobileNetV2, DenseNet and GoogLeNet. The compilation time required for DLNN is more compared with other technique it takes nearly 1 h, 53 min and results 97.3% accuracy in detection of COVID-19.
ISSN:	0971-1198
DOI:	10.5281/zenodo.12186903