TY - GEN
T1 - Compressed VGGNet for Automatic COVID-19 Disease Detection from CT Scan Images
AU - Vinothini, R.
AU - Niranjana, G.
AU - Dhamanti, Inge
AU - Lailiyah, Syifa’ul
AU - Yakub, Fitri
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Classification
KW - COVID-19
KW - Visual Geometry Group (VGGNet)
UR - http://www.scopus.com/inward/record.url?scp=85214118403&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73617-9_9
DO - 10.1007/978-3-031-73617-9_9
M3 - Conference contribution
AN - SCOPUS:85214118403
SN - 9783031736162
T3 - IFIP Advances in Information and Communication Technology
SP - 105
EP - 119
BT - Computer, Communication, and Signal Processing. Smart Solutions Towards SDG - 8th IFIP TC 12 International Conference, ICCCSP 2024, Revised Selected Papers
A2 - Chandrabose, Aravindan
A2 - Fernando, Xavier
A2 - Mercier-Laurent, Eunika
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024
Y2 - 20 March 2024 through 22 March 2024
ER -