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Du e to rapid population growth and the need to use artificial intelligenceto make quick decisions, developing a machine learning-based disease detectionmodel and abnormality identification system has greatly improved the level ofmedical diagnosis Since COVID-19 has become one of the most severe diseases inthe world, developing an automatic COVID-19 detection framework helps medicaldoctors in the diagnostic process of disease and provides correct and fastresults. In this paper, we propose a machine lear ning based framework for thedetection of Covid 19. The proposed model employs a Tsukamoto Neuro FuzzyInference network to identify and distinguish Covid 19 disease from normal andpneumonia cases. While the traditional training methods tune the parameters ofthe neuro-fuzzy model by gradient-based algorithms and recursive least squaremethod, we use an evolutionary-based optimization, the Cat swarm algorithm toupdate the parameters. In addition, six texture features extracted from chestX-ray images are give n as input to the model. Finally, the proposed model isconducted on the chest X-ray dataset to detect Covid 19. The simulation resultsindicate that the proposed model achieves an accuracy of 98.51%, sensitivity of98.35%, specificity of 98.08%, and F1 score of 98.17%.

Evolving Tsukamoto Neuro Fuzzy Model for Multiclass Covid 19 Classification with Chest X Ray Images