Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images

R. Vinothini, G. Niranjana, Inge Dhamanti, Syifa’ul Lailiyah, Fitri Yakub

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Millions of people across the globe were affected by this rapidly spreading disease by the year 2020. Contamination of the respiratory system results from contracting COVID-19. Nonetheless, establishing a conclusive diagnosis of COVID-19 may prove difficult due to the subtle differences it shares with conventional pneumonia and the complexities involved in identifying areas of infection. A growing number of approaches based on deep learning (DL) are being proposed for the automated detection of COVID-19 from CT scan images. By employing the data pretreatment methodology, unprocessed image data becomes prepared for further analysis. The proposed framework utilised transfer learning to construct VGGNet, which is capable of discerning the Covid-19 disease. The model was subsequently assessed in comparison to the most sophisticated models, VGG16 and VGG19, using the SARS-COV2 CT scan dataset. This dataset contains 1230 CT scans of non-infected images and 1252 CT scans of Covid-19-infected images. The model has achieved 99% accuracy, 98% precision, 97% recall, and 98% F1-score, among other performance metrics.

Original languageEnglish
Title of host publicationComputer, Communication, and Signal Processing. Smart Solutions Towards SDG - 8th IFIP TC 12 International Conference, ICCCSP 2024, Revised Selected Papers
EditorsAravindan Chandrabose, Xavier Fernando, Eunika Mercier-Laurent
PublisherSpringer Science and Business Media Deutschland GmbH
Pages105-119
Number of pages15
ISBN (Print)9783031736162
DOIs
Publication statusPublished - 2025
Event8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024 - Chennai, India
Duration: 20 Mar 202422 Mar 2024

Publication series

NameIFIP Advances in Information and Communication Technology
Volume723 IFIP
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024
Country/TerritoryIndia
CityChennai
Period20/03/2422/03/24

Keywords

  • Classification
  • COVID-19
  • Visual Geometry Group (VGGNet)

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