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

Arfan Ghani, Akinyemi Aina, Chan Hwang See, Hongnian Yu, Simeon Keates

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    1 Citation (Scopus)
    8 Downloads (Pure)

    Abstract

    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.
    Original languageEnglish
    Article number1148
    Number of pages15
    JournalElectronics
    Volume11
    Issue number7
    DOIs
    Publication statusPublished - 6 Apr 2022

    Bibliographical note

    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/)

    Funder

    Funding Information: Funding: This research was funded by the American University of Ras Al Khaimah via research grant number ENGR/007/22, United Arab Emirates. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

    Keywords

    • Convolutional Neural Networks (CNN)
    • computer vision
    • reconfigurable architectures
    • intelligent system design
    • COVID-19
    • embedded devices

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