论文标题
COVID-CLNET：通过压缩深度学习方法检测COVID-19检测
COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches
论文作者
论文摘要
美国最严重的全球健康威胁之一是Covid-19大流行。强调改善诊断并提高诊断能力有助于大大阻止其扩散。因此，为了帮助放射科医生或其他医学专家在最短的时间内检测和确定COVID-19病例，我们建议使用计算机辅助检测系统（CADE）系统，该系统使用计算机断层扫描（CT）扫描图像。该提出的增强深度学习网络（CLNET）是基于对压缩学习（CL）的补充的深度学习（DL）网络的实施。我们在访问卷积神经网络之前，使用CL在测量域中利用我们的启动特征提取技术将数据特征表示为具有较小维度的新空间。所有原始功能都使用传感矩阵在新空间中同样贡献。在不同的压缩方法上进行的实验显示了COVID-19检测的有希望的结果。此外，我们基于用于捕获增强功能的不同感应矩阵的新型加权方法表明，该方法的性能有所改善。
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.
