One of the most serious global health threat is COVID-19 pandemic. The emphasis on improving diagnosis and increasing the diagnostic capability helps stopping its spread significantly. Therefore, to assist the radiologist or other medical professional to detect and identify the COVID-19 cases in the shortest possible time, we propose a computer-aided detection (CADe) system that uses the computed tomography (CT) scan images. This proposed boosted deep learning network (CLNet) is based on the implementation of Deep Learning (DL) networks as a complementary to the Compressive Learning (CL). We utilize our inception feature extraction technique in the measurement domain using CL to represent the data features into a new space with less dimensionality before accessing the Convolutional Neural Network. All original features have been contributed equally in the new space using a sensing matrix. Experiments performed on different compressed methods show promising results for COVID-19 detection. In addition, our novel weighted method based on different sensing matrices that used to capture boosted features demonstrates an improvement in the performance of the proposed method.

翻译：最严重的全球健康威胁之一是COVID-19大流行。强调改善诊断和增加诊断能力有助于阻止其扩散。因此,为了协助放射学家或其他医学专业人员在尽可能短的时间内发现和识别COVID-19病例,我们提议建立一个计算机辅助检测系统,使用计算断层摄影图像;这一拟议的促进深层学习网络(CLNet)的基础是实施深层学习网络,作为压缩学习的补充。我们利用CL在测量领域利用我们的初始特征提取技术,在进入动态神经网络之前将数据特征显示为一个新的空间,在进入动态神经网络之前,将数据特征表现为较不具有维度的新空间。所有原始特征均在新空间使用感测矩阵作出同等贡献。在不同压缩方法上进行的实验显示了COVID-19探测的可喜结果。此外,我们基于不同测量振动特征的不同感测矩阵的新加权方法显示了拟议方法的绩效。