Severity of lung infection identification and classification using optimization-enabled deep learning with IoT

A major disease affecting individuals irrespective of the different ages is lung disease and this problem is a result of different causes. The recent spread of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected a huge community worldwide and has impacted the...

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Bibliographic Details
Published in:Multimedia systems 2024-04, Vol.30 (2), Article 107
Main Authors: Vijaya, P., Chander, Satish, Fernandes, Roshan, Rodrigues, Anisha P., Maheswari, R.
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Computer Communication Networks
Computer Graphics
Computer Science
Coronaviruses
COVID-19
Cryptology
Data Storage Representation
Deep learning
Infections
Internet of Things
Machine learning
Multimedia Information Systems
Operating Systems
Optimization
Regular Paper
Respiratory diseases
Respiratory system
Search algorithms
Viral diseases
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Summary:A major disease affecting individuals irrespective of the different ages is lung disease and this problem is a result of different causes. The recent spread of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected a huge community worldwide and has impacted the respiratory system adversely. The infection severity can be determined by inspecting the using X-ray images of the lung. In this work, a multilevel classification approach is presented, wherein the lung severity and COVID-19 prediction are executed based on Deep Learning (DL) technique. The contribution of this research is three-fold: (1) a novel Political Golden Search Algorithm (PGSA) was devised for routing the data accumulated from the nodes over the Internet of Things (IoT), (2) first-level classification was performed using the developed SqueezeNet-based technique, and it is optimized by the devised Taylor Political Golden Search Optimization (TPGSO) algorithm, to detect if COVID-19 is present or not, (3) second-level classification is accomplished using the TPGSO-deep convolutional neural network (DCNN) to categorize lung infection severity. The presented TPGSO-DCNN for second-level classification is examined for its performance based on testing accuracy, test negative rate (TNR), and test positive rate (TPR), and is established to have obtained values of 0.922, 0.926, and 0.909, respectively.
ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-024-01275-2