Towards a Classification Model using CNN and Wavelets applied to COVID-19 CT images
DOI:
https://doi.org/10.33448/rsd-v11i5.27919Keywords:
CT images; Convolutional Neural Networks; COVID-19; Wavelets; WCN-COVID.Abstract
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%.
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Copyright (c) 2022 Pedro Moises de Sousa; Pedro Cunha Carneiro; Gabrielle Macedo Pereira; Mariane Modesto Oliveira; Carlos Aberto da Costa Junior; Luis Vinicius de Moura; Christian Mattjie; Ana Maria da Silva; Túlio Augusto Alves Macedo; Ana Claudia Patrocinio
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