Affiliations 

  • 1 Department of Electrical Engineering, Jadavpur University, Kolkata, 700032 India
  • 2 Department of Information Technology, Jadavpur University, Kolkata, 700106 India
  • 3 Department of Law, Economics and Human Sciences & Decisions Lab, Mediterranea University of Reggio Calabria, Reggio Calabria, 89125 Italy
  • 4 Institute of IR 4.0, The National University of Malaysia, Bangi, 43600 UKM Selangor Malaysia
  • 5 Department of Computer Science & Engineering, Jadavpur University, Kolkata, 700032 India
Multimed Tools Appl, 2022;81(1):31-50.
PMID: 34483709 DOI: 10.1007/s11042-021-11319-8

Abstract

The COVID-19 virus has caused a worldwide pandemic, affecting numerous individuals and accounting for more than a million deaths. The countries of the world had to declare complete lockdown when the coronavirus led to community spread. Although the real-time Polymerase Chain Reaction (RT-PCR) test is the gold-standard test for COVID-19 screening, it is not satisfactorily accurate and sensitive. On the other hand, Computer Tomography (CT) scan images are much more sensitive and can be suitable for COVID-19 detection. To this end, in this paper, we develop a fully automated method for fast COVID-19 screening by using chest CT-scan images employing Deep Learning techniques. For this supervised image classification problem, a bootstrap aggregating or Bagging ensemble of three transfer learning models, namely, Inception v3, ResNet34 and DenseNet201, has been used to boost the performance of the individual models. The proposed framework, called ET-NET, has been evaluated on a publicly available dataset, achieving 97.81 ± 0.53 % accuracy, 97.77 ± 0.58 % precision, 97.81 ± 0.52 % sensitivity and 97.77 ± 0.57 % specificity on 5-fold cross-validation outperforming the state-of-the-art method on the same dataset by 1.56%. The relevant codes for the proposed approach are accessible in: https://github.com/Rohit-Kundu/ET-NET_Covid-Detection.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.