Early detection and diagnosis of COVID-19, as well as 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 detection and diagnosis of COVID-19. Due to the limited availability of RT-PCR (Reverse transcription-polymerase Chain Reaction) test in developing countries, imaging-based techniques could offer an alternative and affordable solution to detect COVID-19 symptoms. This case study 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 for 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.


This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Cite as

Ghani, A., Aina, A., See, C., Yu, H. & Keates, S. 2022, 'Accelerated Diagnosis of Novel Coronavirus (COVID-19)—Computer Vision with Convolutional Neural Networks (CNNs)', Electronics, 11(7), article no: 1148. https://doi.org/10.3390/electronics11071148

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Last updated: 16 June 2022
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