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

Anita S. Kini, A. Nanda Gopal Reddy, Manjit Kaur, S. Satheesh, Jagendra Singh, Thomas Martinetz, Hammam Alshazly*

*Corresponding author for this work


Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The 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 Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The 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. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.

Original languageEnglish
Article number7377502
JournalContrast Media and Molecular Imaging
Publication statusPublished - 2022

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems

Coronavirus related work

  • Research on SARS-CoV-2 / COVID-19


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