论文标题
使用卷积神经网络在X射线图像上自动检测COVID-19案例
Automatic Detection of COVID-19 Cases on X-ray images Using Convolutional Neural Networks
论文作者
论文摘要
近几个月来，全世界对Covid-19的迅速发展感到惊讶。为了面对这种疾病并最大程度地减少其社会经济影响，除了监视和治疗外，诊断是至关重要的程序。但是，由于延迟和对实验室测试的访问有限，要求实现这一目标的实现，要求采取新的策略来执行案例分类。在这种情况下，正在提出深度学习模型，作为基于胸部X射线和计算机断层扫描图像的诊断过程的可能选择。因此，本研究旨在通过深度学习技术使用卷积神经网络（CNN）自动化从胸部图像中检测COVID-19病例的过程。结果可能有助于扩大对Covid-19的其他形式检测的机会，并加快识别该疾病的过程。所有使用的数据库，构建的代码以及从模型培训获得的结果都可以进行开放访问。这项行动促进了其他研究人员在增强这些模型方面的参与，因为这可能有助于改善结果，因此，与Covid-19与Covid-19的进展有助于。
In recent months the world has been surprised by the rapid advance of COVID-19. In order to face this disease and minimize its socio-economic impacts, in addition to surveillance and treatment, diagnosis is a crucial procedure. However, the realization of this is hampered by the delay and the limited access to laboratory tests, demanding new strategies to carry out case triage. In this scenario, deep learning models are being proposed as a possible option to assist the diagnostic process based on chest X-ray and computed tomography images. Therefore, this research aims to automate the process of detecting COVID-19 cases from chest images, using convolutional neural networks (CNN) through deep learning techniques. The results can contribute to expand access to other forms of detection of COVID-19 and to speed up the process of identifying this disease. All databases used, the codes built, and the results obtained from the models' training are available for open access. This action facilitates the involvement of other researchers in enhancing these models since this can contribute to the improvement of results and, consequently, the progress in confronting COVID-19.
