Since the recent challenge that humanity is facing against COVID-19, several initiatives have been put forward with the goal of creating measures to help control the spread of the pandemic. In this paper we present a series of experiments using supervised learning models in order to perform an accurate classification on datasets consisting of medical images from COVID-19 patients and medical images of several other related diseases affecting the lungs. This work represents an initial experimentation using image texture feature descriptors, feed-forward and convolutional neural networks on newly created databases with COVID-19 images. The goal was setting a baseline for the future development of a system capable of automatically detecting the COVID-19 disease based on its manifestation on chest x-rays and computerized tomography images of the lungs.

自人类最近面临新冠肺炎 (COVID-19) 挑战以来，已经提出了多项倡议，旨在制定措施帮助控制这一流行病的传播。在本文中，我们使用监督学习模型进行了一系列实验，以便对由 COVID-19 患者的医学图像和影响肺部的其他几种相关疾病的医学图像组成的数据集进行准确的分类。这项工作代表了在新创建的包含 COVID-19 图像的数据库上使用图像纹理特征描述符、前馈和卷积神经网络的初步实验。目标是为未来开发能够根据胸部 X 光检查和肺部计算机断层扫描图像自动检测 COVID-19 疾病的系统设定基线。