COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches

TL;DR

This paper introduces COVID-CLNet, a novel deep learning system combining compressive learning and CNNs to improve COVID-19 detection from CT scans, achieving promising results with reduced data dimensionality.

Contribution

The paper proposes a new boosted deep learning network integrating compressive learning with inception feature extraction for COVID-19 detection.

Findings

Effective feature representation in compressed domain

Improved detection accuracy with weighted sensing matrices

Promising results on various compressed data methods

Abstract

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.