Can I read Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images on EtoBox?

Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images by Kubilay Muhammed Sunnetci; Ahmet Alkan is a Computer Science article available to read on EtoBox.

What is Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images about?

The COVID-19 pandemic has been affecting the world since December 2019, and nowadays, the number of infected is increasing rapidly. Chest X-ray images are clinical adjuncts that can be used in the diagnosis of COVID-19 disease. Because of the rapid spread of COVID-19 disease worldwide and the limited number of expert radiologists, the proposed method uses the automatic diagnosis method rather than a manual diagnosis method. In the paper, COVID-19 Positive/Negative (2275 Positive, 4626 Negative) and Normal/Pneumonia (2313 Normal, 2313 Pneumonia) are diagnosed using chest X-ray images. Herein, 80 % and 20 % of the images are used in the training and validation set, respectively. In the proposed method, six different classifiers are trained using chest X-ray images, and the five most successful classifiers are used in both phases. In Phase-1 and Phase-2, image features are extracted using the Bag of Features method for Cosine K-Nearest Neighbor (KNN), Linear Discriminant, Logistic Regression, Bagged Trees Ensemble, Medium Gaussian Support Vector Machine (SVM), excluding SqueezeNet Deep Learning (K = 2000 and K = 1500 for Phase-1 and Phase-2, respectively). In both phases, the five mos

Who reads Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images?

Author

Kubilay Muhammed Sunnetci; Ahmet Alkan

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)