Open AccessClassification by a stacking model using CNN features for COVID-19 infection diagnosis
Author(s) -
Yavuz Selim Taşpınar,
İ̇lkay Çinar,
Murat Köklü
Publication year - 2022
Publication title -
journal of x-ray science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.357
H-Index - 32
eISSN - 1095-9114
pISSN - 0895-3996
DOI - 10.3233/xst-211031
Subject(s) - covid-19 , support vector machine , artificial intelligence , stacking , machine learning , computer science , pneumonia , artificial neural network , precision and recall , pattern recognition (psychology) , medicine , disease , pathology , infectious disease (medical specialty) , physics , nuclear magnetic resonance
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.