Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q3
Classification by a stacking model using CNN features for COVID-19 infection diagnosis
Journal of X Ray Science and Technology · Ocak 2022
YÖKSİS Kayıtları
Classification by a stacking model using CNN features for COVID-19 infection diagnosis
Journal of X-Ray Science and Technology · 2022 SCI-Expanded
Dr. Öğr. Üyesi İLKAY ÇINAR →
Classification by a stacking model using CNN features for COVID-19 infection diagnosis
Journal of X-Ray Science and Technology · 2022 SCI-Expanded
Doç. Dr. YAVUZ SELİM TAŞPINAR →
Classification by a Stacking Model Using CNN Features for COVID-19 Infection Diagnosis
Journal of X-Ray Science and Technology · 2022 SCI-Expanded
Doç. Dr. MURAT KÖKLÜ →
YÖKSİS Kayıtları — ISSN Eşleşmesi
Classification by a Stacking Model Using CNN Features for COVID-19 Infection Diagnosis
2022 ISSN: 0895-3996 SCI-Expanded Q3
Doç. Dr. MURAT KÖKLÜ →
Comparative analysis of machine learning and deep learning algorithms for knee arthritis detection using YOLOv8 models
2025 ISSN: 0895-3996 SCI-Expanded Q3
Dr. Öğr. Üyesi İLKAY ÇINAR →
Makale Bilgileri
ISSN08953996
Yayın TarihiOcak 2022
Cilt / Sayfa30 · 73-88
Scopus ID2-s2.0-85123904573
Erişim🔓 Açık Erişim
Özet
Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumonia left behind by the virus may still cause severe diseases resulting in organ failure and therefore death in the future. The aim of this study is to classify COVID-19, normal and viral pneumonia using the chest X-ray images with machine learning methods. A total of 3486 chest X-ray images from three classes were first classified by three single machine learning models including the support vector machine (SVM), logistics regression (LR), artificial neural network (ANN) models, and then by a stacking model that was created by combining these 3 single models. Several performance evaluation indices including recall, precision, F-1 score, and accuracy were computed to evaluate and compare classification performance of 3 single four models and the final stacking model used in the study. As a result of the evaluations, the models namely, SVM, ANN, LR, and stacking, achieved 90.2%, 96.2%, 96.7%, and 96.9%classification accuracy, respectively. The study results indicate that the proposed stacking model is a fast and inexpensive method for assisting COVID-19 diagnosis, which can have potential to assist physicians and nurses to better and more efficiently diagnose COVID-19 infection cases in the busy clinical environment.
Yazarlar (3)
1
Yavuz Selim Taspinar
ORCID: 0000-0002-7278-4241
2
Ilkay Cinar
ORCID: 0000-0003-0611-3316
3
Murat Koklu
ORCID: 0000-0002-2737-2360
Anahtar Kelimeler
Convolutional neural network
COVID-19
Stacking model
X-ray chest images
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of X-Ray Science and Technology
Q3
SJR Skoru0,312
H-Index41
YayıncıSAGE Publications Ltd
ÜlkeNetherlands
Condensed Matter Physics (Q3)
Electrical and Electronic Engineering (Q3)
Instrumentation (Q3)
Radiation (Q3)
Radiology, Nuclear Medicine and Imaging (Q3)
Metrikler
52
Atıf
3
Yazar
4
Anahtar Kelime