Evolving Tsukamoto Neuro Fuzzy Model for Multiclass Covid 19
  Classification with Chest X Ray Images
Marziyeh Rezaei Sevda Molani Negar Firoozeh Hossein Abbasi Farzan Vahedifard Maysam Orouskhani
Du e to rapid population growth and the need to use artificial intelligence
to make quick decisions, developing a machine learning-based disease detection
model and abnormality identification system has greatly improved the level of
medical diagnosis Since COVID-19 has become one of the most severe diseases in
the world, developing an automatic COVID-19 detection framework helps medical
doctors in the diagnostic process of disease and provides correct and fast
results. In this paper, we propose a machine lear ning based framework for the
detection of Covid 19. The proposed model employs a Tsukamoto Neuro Fuzzy
Inference network to identify and distinguish Covid 19 disease from normal and
pneumonia cases. While the traditional training methods tune the parameters of
the neuro-fuzzy model by gradient-based algorithms and recursive least square
method, we use an evolutionary-based optimization, the Cat swarm algorithm to
update the parameters. In addition, six texture features extracted from chest
X-ray images are give n as input to the model. Finally, the proposed model is
conducted on the chest X-ray dataset to detect Covid 19. The simulation results
indicate that the proposed model achieves an accuracy of 98.51%, sensitivity of
98.35%, specificity of 98.08%, and F1 score of 98.17%.