The COVID-19 (Coronavirus disease 2019) has infected more than 151 million people and caused approximately 3.17 million deaths around the world up to the present. The rapid spread of COVID-19 is continuing to threaten human's life and health. Therefore, the development of computer-aided detection (CAD) systems based on machine and deep learning methods which are able to accurately differentiate COVID-19 from other diseases using chest computed tomography (CT) and X-Ray datasets is essential and of immediate priority. Different from most of the previous studies which used either one of CT or X-ray images, we employed both data types with sufficient samples in implementation. On the other hand, due to the extreme sensitivity of this pervasive virus, model uncertainty should be considered, while most previous studies have overlooked it. Therefore, we propose a novel powerful fusion model named $UncertaintyFuseNet$ that consists of an uncertainty module: Ensemble Monte Carlo (EMC) dropout. The obtained results prove the effectiveness of our proposed fusion for COVID-19 detection using CT scan and X-Ray datasets. Also, our proposed $UncertaintyFuseNet$ model is significantly robust to noise and performs well with the previously unseen data. The source codes and models of this study are available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.

翻译：COVID-19(Corona病毒疾病,2019年)已经感染了1.51亿多人,并在全世界造成大约317万人死亡,COVID-19的迅速扩散继续威胁着人的生命和健康,因此,在机器和深层学习方法的基础上开发计算机辅助检测系统(CAD),能够精确地将COVID-19与其他疾病区别开来,使用胸部计色透析(CT)和X光数据集至关重要,是当前的优先事项。与以前使用CT或X光图像之一或X光图像的大多数研究不同,我们使用了具有足够样本的数据类型。另一方面,由于这种普遍的病毒极为敏感,模型的不确定性应该加以考虑,而大多数先前的研究忽视了这一点。因此,我们提出了一个新的强效聚合模型,名为“UncertfertfortyFuseNet$”,由不确定性模块组成:Entsemble Montecar(EMC)的辍学。获得的结果证明,我们提议的COVID-19探测CO-19的聚合使用CT扫描模型和X-DNUC-RO-OF数据库的有效性。我们提出的数据来源是牢固的。