Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework

Kini, Anita S and Reddy, Nanda Gopal and Kaur, Manjit and Satheesh, S and Singh, Jagendra and Martinetz, Thomas and Alshazly, Hammam (2022) Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework. Contrast Media & Molecular Imaging. ISSN 1555-4309

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Abstract

Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. +e modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of +ings (IoT) based framework is proposed for screening of COVID-19 suspected cases. +ree well-known pretrained deep learning models are ensembled. +e medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. +e proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. +erefore, the proposed framework can improve the acceleration of COVID-19 diagnosis

Item Type: Article
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 12 Jul 2022 04:50
Last Modified: 12 Jul 2022 04:50
URI: http://eprints.manipal.edu/id/eprint/158917

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