  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.
