Scopus
YÖKSİS Eşleşti
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Journal of Experimental and Theoretical Artificial Intelligence · Ocak 2024
YÖKSİS Kayıtları
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Journal of Experimental & Theoretical Artificial Intelligence · 2024 SCI-Expanded
DOÇENT HAKAN CEBECİ →
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Informa UK Limited · 2022 SCI-Expanded
PROFESÖR FİKRET KANAT →
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Journal of Experimental & Theoretical Artificial Intelligence · 2024 SCI-Expanded
PROFESÖR FİKRET KANAT →
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Informa UK Limited · 2022 SCI-Expanded
PROFESÖR MUSTAFA KOPLAY →
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Journal of Experimental & Theoretical Artificial Intelligence · 2022 SCI-Expanded
DOÇENT HAKAN CEBECİ →
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Journal of Experimental & Theoretical Artificial Intelligence · 2022 SCI-Expanded
PROFESÖR FİKRET KANAT →
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Journal of Experimental & Theoretical Artificial Intelligence · 2022 SCI-Expanded
PROFESÖR MUSTAFA KOPLAY →
A novel study to increase the classification parameters on automatic three-class COVID-19 classification from CT images, including cases from Turkey
Journal of Experimental & Theoretical Artificial Intelligence · 2022 SCI-Expanded
DOÇENT ABİDİN KILINÇER →
Makale Bilgileri
DergiJournal of Experimental and Theoretical Artificial Intelligence
Yayın TarihiOcak 2024
Cilt / Sayfa36 · 563-583
Scopus ID2-s2.0-85133294914
Özet
A computed tomography (CT) scan is an important radiological imaging method in diagnosing pneumonia caused by SARS-CoV-2. Within the scope of the study, three classes of automatic classification–COVID-19 pneumonia, healthy, and other pneumonia–were carried out. Using deep learning as a classifier, a total of 6,377 CT images were used, including 3,364 COVID-19 pneumonia, 1,766 healthy, and 1,247 other pneumonia images. A total of seven architectures, including the most recent convolutional neural network (CNN) architectures, MobileNetV2, ResNet-101, Xception, Inceptionv3, GoogLeNet, EfficientNetB0, and DenseNet201, were used in the study. The classification results were obtained using the CT images, and they were calculated using the feature images obtained by applying local binary patterns on the CT images. The results were then combined with the help of a pipeline algorithm. The results revealed that the best overall accuracy result obtained by using CNN architectures could be improved by 4.87% with a two-step pipeline algorithm. In addition, significant improvements were achieved in all other measurement parameters within the scope of the study. At the end of the study, the highest sensitivity, specificity, accuracy, F-1 score, and Area under the Receiver Operating Characteristic Curve (AUC) values obtained for the COVID-19 pneumonia class were 0.9004, 0.8901, 0.8956, 0.9010, and 0.9600, respectively. The highest overall accuracy value was 0.8332. The most important output of the work carried out is the demonstration that the results obtained with the most successful CNN architectures used in previous studies can be significantly improved thanks to pipeline algorithms.
Yazarlar (6)
1
Hüseyin Yaşar
2
Murat Ceylan
3
Hakan Cebeci
4
Abidin Kılınçer
5
F. Kanat
6
M. Koplay
ORCID: 0000-0001-7513-4968
Anahtar Kelimeler
convolutional neural networks
COVID-19
CT lung classification
deep learning
DenseNet201
Inceptionv3
local binary patterns
Kurumlar
Konya Technical University
Konya Turkey
Selçuk Tip Fakültesi
Konya Turkey
T.C. Sağlık Bakanlığı,
Ankara Turkey
Metrikler
1
Atıf
6
Yazar
7
Anahtar Kelime