An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

Ogechukwu Ukwandu; Hanan Hindy; Elochukwu Ukwandu

doi:10.1016/j.health.2022.100096

An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

Healthc Anal (N Y). 2022 Nov:2:100096. doi: 10.1016/j.health.2022.100096. Epub 2022 Aug 23.

Authors

Ogechukwu Ukwandu^{1

2}, Hanan Hindy³, Elochukwu Ukwandu⁴

Affiliations

¹ Department of Computer, Communication and Information Systems, School of Engineering and Built Environment, Glasgow Caledonian University, UK.
² School of Computer Science and Informatics, Cardiff University, Cardiff, Wales.
³ Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt.
⁴ Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK.

Abstract

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.

Keywords: COVID-19 detection; Diagnostic analytics; Lightweight deep learning techniques; Machine learning; Point-of-care; Resource-constrained devices.