Ctcovid19: Automatic Covid-19 Model for Computed Tomography Scans Using Deep Learning

14 Pages Posted: 19 Aug 2024

See all articles by Carlos Antunes

Carlos Antunes

affiliation not provided to SSRN

João M. F. Rodrigues

University of Algarve

António Cunha

affiliation not provided to SSRN

Abstract

Summary COVID-19 is an extremely contagious respiratory sickness instigated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Common symptoms encompass fever, cough, fatigue, and breathing difficulties, often leading to hospitalization and fatalities in severe cases. CTCovid19 is a novel model tailored for COVID-19 detection, specifically honing in on a distinct deep learning structure, ResNet-50 trained with ImageNet serves as the foundational framework for our model. To enhance its capability to capture pertinent features related to COVID-19 patterns in Computed Tomography scans, the network underwent fine-tuning through layer adjustments and the addition of new ones. The model achieved accuracy rates that went from 97.0% to 99.8% across three widely recognized and documented datasets dedicated to COVID-19 detection.

Note:
Funding Information: This work was supported by the Portuguese Foundation for Science and Technology (FCT) project NOVA LINCS (UIDB/04516/2020) and by FCT project ALGORITMI (UIDB/00319/2020).

Declaration of Interests: There is no conflict of interest.

Keywords: Deep Learning, CT scans, COVID-19, convolutional neural network (CNN), and Explainable Artificial Intelligence (XAI)

Suggested Citation

Antunes, Carlos and Rodrigues, João M. F. and Cunha, António, Ctcovid19: Automatic Covid-19 Model for Computed Tomography Scans Using Deep Learning. Available at SSRN: https://ssrn.com/abstract=4925383

Carlos Antunes (Contact Author)

affiliation not provided to SSRN ( email )

No Address Available

João M. F. Rodrigues

University of Algarve ( email )

8000-117 Faro
Portugal

António Cunha

affiliation not provided to SSRN ( email )

No Address Available

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