A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
A novel Covid-19 and Pneumonia Classification Method based on F-transform
Tekijät: Tuncer Turker, Ozyurt Fatih, Dogan Sengul, Subasi Abdulhamit
Kustantaja: Elsevier
Julkaisuvuosi: 2021
Journal: Chemometrics and Intelligent Laboratory Systems
Artikkelin numero: 104256
Vuosikerta: 210
DOI: https://doi.org/10.1016/j.chemolab.2021.104256
Verkko-osoite: https://www.sciencedirect.com/science/article/pii/S0169743921000241
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/53055974
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.
Ladattava julkaisu This is an electronic reprint of the original article. |