Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-06-16

AUTHORS

Chandrakanta Mahanty, Raghvendra Kumar, S. Gopal Krishna Patro

ABSTRACT

One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders. More... »

PAGES

1-17

References to SciGraph publications

  • 2021-02-01. Diagnosis of COVID-19 using CT scan images and deep learning techniques in EMERGENCY RADIOLOGY
  • 2020-11-05. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography in SCIENTIFIC REPORTS
  • 2021-04-15. RETRACTED ARTICLE: GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest in SCIENTIFIC REPORTS
  • 2020-05-20. Chest computed tomography findings of coronavirus disease 2019 (COVID-19) pneumonia in EUROPEAN RADIOLOGY
  • 2021-07-08. Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans in SCIENTIFIC REPORTS
  • 2021-11-16. Artificial Intelligence and an Edge-IoMT-Based System for Combating COVID-19 Pandemic in INTELLIGENT INTERACTIVE MULTIMEDIA SYSTEMS FOR E-HEALTHCARE APPLICATIONS
  • 2000-12-01. Ensemble Methods in Machine Learning in MULTIPLE CLASSIFIER SYSTEMS
  • 2022-01-12. Application of Deep Learning Techniques for Detection of COVID-19 Using Lung CT Scans: Model Development and Validation in INTERNATIONAL YOUTH CONFERENCE ON ELECTRONICS, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGIES
  • 2020-08-12. Rapid identification of COVID-19 severity in CT scans through classification of deep features in BIOMEDICAL ENGINEERING ONLINE
  • 2021-06-28. Deep learning empowered COVID-19 diagnosis using chest CT scan images for collaborative edge-cloud computing platform in MULTIMEDIA TOOLS AND APPLICATIONS
  • 2021-08-18. Detection of COVID-19 Using EfficientNet-B3 CNN and Chest Computed Tomography Images in INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING AND COMMUNICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00354-022-00176-0

    DOI

    http://dx.doi.org/10.1007/s00354-022-00176-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1148730830

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35730008


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