In 2019, the world faced a new challenge: a COVID-19 disease caused by the novel coronavirus, SARS-CoV-2. The virus rapidly spread across the globe, leading to a high rate of mortality, which prompted health organizations to take measures to control its transmission. Early disease detection is crucial in the treatment process, and computer-based automatic detection systems have been developed to aid in this effort. These systems often rely on artificial intelligence (AI) approaches such as machine learning, neural networks, fuzzy systems, and deep learning to classify diseases. This study aimed to differentiate COVID-19 patients from others using self-categorizing classifiers and employing various AI methods. This study used two datasets: the blood test samples and radiography images. The best results for the blood test samples obtained from San Raphael Hospital, which include two classes of individuals, those with COVID-19 and those with non-COVID diseases, were achieved through the use of the Ensemble method (a combination of a neural network and two machines learning methods). The results showed that this approach for COVID-19 diagnosis is cost-effective and provides results in a shorter amount of time than other methods. The proposed model achieved an accuracy of 94.09% on the dataset used. Secondly, the radiographic images were divided into four classes: normal, viral pneumonia, ground glass opacity, and COVID-19 infection. These were used for segmentation and classification. The lung lobes were extracted from the images and then categorized into specific classes. We achieved an accuracy of 91.1% on the image dataset. Generally, this study highlights the potential of AI in detecting and managing COVID-19 and underscores the importance of continued research and development in this field.

本研究旨在使用自分类分类器，结合各种人工智能方法，通过血液检测样本和放射学图像将COVID-19患者与其他人区分开。提出的模型在数据集上实现了94.09％的准确率，并在更短时间内提供结果。此外，还将放射学图像分为四类，并通过提取肺叶并进行分类来实现了91.1％的准确率。总的来说，本研究强调了人工智能在COVID-19的检测和管理中的潜力，并强调了在该领域继续进行研究和发展的重要性。

基于不同人工智能方法的血液检测参数的新冠病毒检测