The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), there have been more than 100 million confirmed cases of COVID-19, including more than 2.4 million deaths. It is extremely important the early detection of the disease, and the use of medical imaging such as chest X-ray (CXR) and chest Computed Tomography (CCT) have proved to be an excellent solution. However, this process requires clinicians to do it within a manual and time-consuming task, which is not ideal when trying to speed up the diagnosis. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify pneumonia patterns while providing information about the reliability of the classification. Specifically, each CCT scan is divided into cubic patches and features contained in each one of them are extracted by applying kernel PCA. The use of base classifiers within an ensemble allows our system to identify the pneumonia patterns regardless of their size or location. Decisions of each individual patch are then combined into a global one according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would result in a huge computational cost) evidence the applicability of our proposal in a real-world environment.

翻译：COVID-19(Corona病毒疾病 2019)的爆发改变了世界。根据世界卫生组织(世卫组织)的资料,已有1亿多经证实的COVID-19病例,包括240多万人死亡。早期发现该疾病极为重要,使用胸X光(CXR)和胸腔合成托声学(CCT)等医疗成像证明是一个极好的解决方案。然而,这一过程要求临床医生在人工和耗时的工作范围内做这项工作,这在加快诊断速度时是不理想的。在这项工作中,我们提出一个基于概率支持Vctor机(SVM)的混合分类器,以确定肺炎模式,同时提供有关分类可靠性的信息。具体地说,每部CCT扫描都分为立三次,每部的特征都是通过使用内脏五氯苯来提取的。使用一个组合中的基级分类器将使我们的系统能够识别肺炎模式,而不管其大小或地点如何。在试图加速诊断时,每个系统都很难做到。在这个系统中,每个更高级的分类方法的大小或位置都组合成一个总的精度的精度,然后在一个深度的精度的精确度中,一个全球的精确度的精确度将一个精确度的精确度纳入一个精确度的精确度的精确度。 。每个过程的精确度的精确度的精确度的精确度,每个过程的精确度的精确度的精确度的精确度是每个过程的精确度的精确度的精确度的精确度的精确度的精确度的精确度的精确度的精确度。