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%.

翻译：随着人口的快速增长以及利用人工智能进行快速决策的需求，开发基于机器学习技术的疾病检测模型与异常识别系统显著提升了医疗诊断水平。鉴于COVID-19已成为全球最严重的疾病之一，构建自动化COVID-19检测框架有助于医生在疾病诊断过程中获得准确且快速的结果。本文提出了一种基于机器学习的COVID-19检测框架。该模型采用冢本神经模糊推理网络，用于识别并区分正常、肺炎与COVID-19病例。传统训练方法通过基于梯度的算法和递归最小二乘法调整神经模糊模型参数，而本研究采用基于进化的优化方法——猫群算法来更新参数。此外，从胸部X光图像中提取的六种纹理特征被输入模型。最终，在胸部X光数据集上进行COVID-19检测实验。仿真结果表明，所提模型达到了98.51%的准确率、98.35%的敏感性、98.08%的特异性以及98.17%的F1分数。