Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.

冠状病毒可引起多种呼吸道感染，它是一种RNA型病毒，可​​同时感染人类和动物。它通常在人类中引起肺炎。人工智能模型对于生物医学领域的成功分析很有帮助。在这项研究中，使用深度学习模型检测了冠状病毒，该模型是人工智能的一个分支。我们的数据集包括三类：冠状病毒，肺炎和正常的X射线图像。在这项研究中，使用模糊色彩技术将数据类别重构为预处理步骤，并将原始图像构成的图像堆叠在一起。下一步，使用深度学习模型（MobileNetV2，SqueezeNet）和通过模型获得的特征集使用社交模仿优化方法进行处理。此后，使用支持向量机（SVM）对有效特征进行组合和分类。提出的方法获得的总分类率为99.27％。通过本研究中提出的方法，很明显该模型可以有效地帮助检测COVID-19疾病。