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COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach

COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach

Khalfalla Awedat, Almabrok Essa
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 16
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781683182122|DOI: 10.4018/IJCVIP.2022010105
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MLA

Awedat, Khalfalla, and Almabrok Essa. "COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach." IJCVIP vol.12, no.1 2022: pp.1-16. http://doi.org/10.4018/IJCVIP.2022010105

APA

Awedat, K. & Essa, A. (2022). COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach. International Journal of Computer Vision and Image Processing (IJCVIP), 12(1), 1-16. http://doi.org/10.4018/IJCVIP.2022010105

Chicago

Awedat, Khalfalla, and Almabrok Essa. "COVID-CLNet: COVID-19 Detection With Compressive Deep Learning Approach," International Journal of Computer Vision and Image Processing (IJCVIP) 12, no.1: 1-16. http://doi.org/10.4018/IJCVIP.2022010105

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Abstract

One of the most serious global health threats is COVID-19 pandemic. The emphasis on 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 to the new space using a sensing matrix. Experiments performed on different compressed methods show promising results for COVID-19 detection.