Akademik Veri Yönetim Sistemi
Artificial Intelligence Theory and Applications, cilt.2, sa.1, ss.280-287, 2021 (Hakemli Dergi)
In the last report of World Health Organization 2021, it has stated that
290,000 to 650,000 people may die each year with respiratory diseases
caused by seasonal influenza. Additionally, it predicts that influenza will
result in more deaths than other illnesses such as flu-related cardiovascular
diseases. Sars-Cov-2 virus is also the deadly respiratory disease virus and has
different mutations.
In 2020, about 1.8 million people died with the Covid-19 virus. The fastest
detection and treatment of the disease should be initiated in the fight against
COVID-19. The most important indicator that can be used in this regard is
radiological data.
From this point of view, artificial intelligence (AI) systems based on deep
learning will make it easier for radiologists to diagnose the disease in this
process. Therefore, in this study, deep convolutional neural networks were
used to detect COVID-19 cases from up to date chest radiography images
with open source access.
DenseNet and SqueezeNet algorithms were used in data set for classification
and feature extraction. X-ray images of normal and COVID-19 cases are
scaled to 224x224 and the data set is divided into 80% training and 20%
testing. The data augmentation process was applied to the images by making
angular change, brightness change, horizontal and vertical shifts.
In classification with DenseNet and SqueezeNet algorithms, high accuracy
values of 99.09% and 97.7%, respectively, were obtained by applying 30
epochs.
The results obtained have shown that artificial intelligence algorithms give
very high accuracy results in detecting COVID-19 patients using chest X-ray
radiography images