Open AccessTowards a Classification Model using CNN and Wavelets applied to COVID-19 CT images
Author(s) -
Pedro Moisés de Sousa,
Pedro Cunha Carneiro,
Gabrielle Macedo Pereira,
Mariane Modesto Oliveira,
Carlos Aberto da Costa,
Luís Vinícius de Moura,
Christian Mattjie,
Ana Maria Marques da Silva,
Thiago Andrade Macedo,
Ana Cláudia Patrocínio
Publication year - 2022
Publication title -
research, society and development
Language(s) - English
Resource type - Journals
ISSN - 2525-3409
DOI - 10.33448/rsd-v11i5.27919
Subject(s) - confusion matrix , covid-19 , receiver operating characteristic , confusion , artificial intelligence , metric (unit) , pattern recognition (psychology) , wavelet , image (mathematics) , wavelet transform , sensitivity (control systems) , mathematics , computer science , medicine , statistics , pathology , disease , psychology , operations management , electronic engineering , outbreak , infectious disease (medical specialty) , psychoanalysis , economics , engineering
In late 2019, a new type of coronavirus emerged in China and was named SARS-CoV-2. It first impacted the country where it emerged and then spread around the world. SARS-CoV-2 is the cause of COVID-19 disease that leaves characteristic impressions on chest CT images of infected patients. In this article, we propose a classification model, based on CNN and wavelet transform, to classify images of COVID-19 patients. It was named WCNN-COVID. The model was applied and tested in open and private TC image repositories. A total of 25534 images of 200 patients were processed. The confusion matrix was generated by calculating Accuracy (ACC), Sensitivity (Sen) and Specificity (Sp). The Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUCs) were also plotted and used for evaluation. Metric results were ACC = 0.9950, Sen = 99.16% and Sp = 99.89%.