论文标题
COVID-19疾病诊断中深机学习算法的比较
A comparison of deep machine learning algorithms in COVID-19 disease diagnosis
论文作者
论文摘要
工作的目的是使用深层神经网络模型来解决图像识别问题。如今，每个人都受到有害冠状病毒疾病的威胁，也称为Covid-19疾病。冠状病毒的传播影响世界许多国家的经济。尽早找到Covid-19患者对于避免对社会的传播和伤害至关重要。病理测试和色谱（CT）扫描有助于诊断Covid-19。但是，这些测试的缺点，例如大量的误报，这些测试的成本是如此昂贵。因此，它需要找到一种容易，准确，更便宜的方法来检测有害的Covid-19疾病。胸x射线可用于检测该疾病。因此，在此工作箱中，使用现代机器学习技术诊断X射线图像来诊断可疑的Covid-19患者。对结果进行分析，并得出关于图像识别问题中深机学习算法的有效性的结论。
The aim of the work is to use deep neural network models for solving the problem of image recognition. These days, every human being is threatened by a harmful coronavirus disease, also called COVID-19 disease. The spread of coronavirus affects the economy of many countries in the world. To find COVID-19 patients early is very essential to avoid the spread and harm to society. Pathological tests and Chromatography(CT) scans are helpful for the diagnosis of COVID-19. However, these tests are having drawbacks such as a large number of false positives, and cost of these tests are so expensive. Hence, it requires finding an easy, accurate, and less expensive way for the detection of the harmful COVID-19 disease. Chest-x-ray can be useful for the detection of this disease. Therefore, in this work chest, x-ray images are used for the diagnosis of suspected COVID-19 patients using modern machine learning techniques. The analysis of the results is carried out and conclusions are made about the effectiveness of deep machine learning algorithms in image recognition problems.
