Accelerated Diagnosis of Novel Coronavirus (COVID-19)—Computer Vision with Convolutional Neural Networks (CNNs)

Ghani, A., Aina, A., See, C. H., Yu, H. and Keates, S. (2022) Accelerated Diagnosis of Novel Coronavirus (COVID-19)—Computer Vision with Convolutional Neural Networks (CNNs). Electronics, 11 (7). ISSN 2079-9292

[thumbnail of © 2022 by the authors. Licensee MDPI, Basel, Switzerland.] Text (© 2022 by the authors. Licensee MDPI, Basel, Switzerland.)
electronics-11-01148.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (5MB)


Early detection and diagnosis of COVID-19, as well as the exact separation of non-COVID-19 cases in a non-invasive manner in the earliest stages of the disease, are critical concerns in the current COVID-19 pandemic. Convolutional Neural Network (CNN) based models offer a remarkable capacity for providing an accurate and efficient system for the detection and diagnosis of COVID-19. Due to the limited availability of RT-PCR (Reverse transcription-polymerase Chain Reaction) tests in developing countries, imaging-based techniques could offer an alternative and affordable solution to detect COVID-19 symptoms. This paper reviewed the current CNN-based approaches and investigated a custom-designed CNN method to detect COVID-19 symptoms from CT (Computed Tomography) chest scan images. This study demonstrated an integrated method to accelerate the process of classifying CT scan images. In order to improve the computational time, a hardware-based acceleration method was investigated and implemented on a reconfigurable platform (FPGA). Experimental results highlight the difference between various approximations of the design, providing a range of design options corresponding to both software and hardware. The FPGA-based implementation involved a reduced pre-processed feature vector for the classification task, which is a unique advantage of this particular application. To demonstrate the applicability of the proposed method, results from the CPU-based classification and the FPGA were measured separately and compared retrospectively.

Publication Type: Articles
Additional Information: This is an open access article under the terms of the Creative Commons Attribution License, which permits use and distribution in any medium, provided the original work is properly cited. You must provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Uncontrolled Keywords: Convolutional Neural Networks (CNN), computer vision, reconfigurable architectures, intelligent system design, COVID-19, embedded devices
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Academic Areas > Department of Engineering, Computing and Design > Electrical Engineering
Academic Areas > Vice Chancellor's Group
Related URLs:
SWORD Depositor: Publications Router Jisc
Depositing User: Publications Router Jisc
Date Deposited: 22 Apr 2022 11:45
Last Modified: 22 Apr 2022 11:45

Actions (login required)

View Item
View Item
▲ Top

Our address

I’m looking for