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

• Published in: Contrast media and molecular imaging Vol. 2022; no. 1; p. 7377502
• Main Authors: Kini, Anita S., Gopal Reddy, A. Nanda, Kaur, Manjit, Satheesh, S., Singh, Jagendra, Martinetz, Thomas, Alshazly, Hammam
• Format: Journal Article
• Language: English
• Published: England Hindawi 2022
• Subjects: COVID-19 - diagnostic imaging; COVID-19 Testing; Female; Humans; Male; Neural Networks, Computer; SARS-CoV-2; Tomography, X-Ray Computed
• ISSN: 1555-4309; 1555-4317; 1555-4317
• DOI: 10.1155/2022/7377502

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.