Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of automated systems for the detection of infections. Decision support systems relax the challenges inherent to the physical examination of images, which is both time consuming and requires interpretation by highly trained clinicians. A review of relevant reported studies to date shows that most deep learning algorithms utilised approaches are not amenable to implementation on resource-constrained devices. Given the rate of infections is increasing, rapid, trusted diagnoses are a central tool in the management of the spread, mandating a need for a low-cost and mobile point-of-care detection systems, especially for middle- and low-income nations. The paper presents the development and evaluation of the performance of lightweight deep learning technique for the detection of COVID-19 using the MobileNetV2 model. Results demonstrate that the performance of the lightweight deep learning model is competitive with respect to heavyweight models but delivers a significant increase in the efficiency of deployment, notably in the lowering of the cost and memory requirements of computing resources.

翻译：及时快速的诊断是指导制定遏制COVID-19传播的最佳干预措施的核心。利用胸部X光片和CT等医学影像辅助逆转录聚合酶链反应（RT-PCR）检测的方法已获倡导，这进而推动了深度学习技术在感染检测自动化系统开发中的应用。决策支持系统缓解了影像人工判读固有的挑战——该过程既耗时又需由高资质临床医生进行解读。对迄今相关研究报告的综述表明，大多数深度学习算法所采用的方法不适合在资源受限设备上部署。鉴于感染率持续上升，快速可信的诊断是控制传播的核心工具，这要求发展低成本、可移动的即时检测系统，尤其对中低收入国家而言。本文介绍了基于MobileNetV2模型开发轻量级深度学习技术用于COVID-19检测的过程及其性能评估。结果表明，轻量级深度学习模型的性能与重量级模型相比具有竞争力，同时显著提高了部署效率，特别是在降低计算资源的成本与内存需求方面。