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

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Aravind Jadhav
Sanjay Pujari

Abstract

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 1h, 53min and results 97.3% accuracy in detection of COVID-19.

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Original Research Article

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