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

Abstract

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.

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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/).

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DOI

https://doi.org/10.3390/electronics11071148

Repository URI

http://researchrepository.napier.ac.uk/Output/2861022