COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches
This work aims to assist radiologists and medical professionals in rapidly detecting COVID-19 from CT scans, offering an incremental improvement in diagnostic capability.
The paper proposes COVID-CLNet, a computer-aided detection system that uses CT scan images to detect COVID-19 cases. It combines Deep Learning with Compressive Learning to represent data features in a lower-dimensional space before CNN processing, showing promising results in experiments.
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.