A1 Refereed original research article in a scientific journal

A novel Covid-19 and Pneumonia Classification Method based on F-transform




AuthorsTuncer Turker, Ozyurt Fatih, Dogan Sengul, Subasi Abdulhamit

PublisherElsevier

Publication year2021

JournalChemometrics and Intelligent Laboratory Systems

Article number104256

Volume210

DOIhttps://doi.org/10.1016/j.chemolab.2021.104256

Web address https://www.sciencedirect.com/science/article/pii/S0169743921000241

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/53055974


Abstract

Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.


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Last updated on 2024-26-11 at 18:03