Open AccessCOVID-19 Diagnosis with Deep Learning
Author(s) -
Hatice Çatal Reis
Publication year - 2021
Publication title -
ingeniería e investigación/ingeniería e investigación
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.204
H-Index - 12
eISSN - 2248-8723
pISSN - 0120-5609
DOI - 10.15446/ing.investig.v42n1.88825
Subject(s) - convolutional neural network , artificial intelligence , computer science , python (programming language) , precision and recall , covid-19 , deep learning , f1 score , binary classification , pattern recognition (psychology) , receiver operating characteristic , artificial neural network , machine learning , medicine , support vector machine , pathology , disease , infectious disease (medical specialty) , operating system
The coronavirus disease 2019 (COVID-19) is fatal and spreading rapidly. Early detection and diagnosis of the COVID-19 infection will prevent rapid spread. This study aims to automatically detect COVID-19 through a chest computed tomography (CT) dataset. The standard models for automatic COVID-19 detection using raw chest CT images are presented. This study uses convolutional neural network (CNN), Zeiler and Fergus network (ZFNet), and dense convolutional network-121 (DenseNet121) architectures of deep convolutional neural network models. The proposed models are presented to provide accurate diagnosis for binary classification. The datasets were obtained from a public database. This retrospective study included 757 chest CT images (360 confirmed COVID-19 and 397 non-COVID-19 chest CT images). The algorithms were coded using the Python programming language. The performance metrics used were accuracy, precision, recall, F1-score, and ROC-AUC. Comparative analyses are presented between the three models by considering hyper-parameter factors to find the best model. We obtained the best performance, with an accuracy of 94,7%, a recall of 90%, a precision of 100%, and an F1-score of 94,7% from the CNN model. As a result, the CNN algorithm is more accurate and precise than the ZFNet and DenseNet121 models. This study can present a second point of view to medical staff.