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

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)