An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging
  for High Precision COVID-19 Diagnostics
Ogechukwu Ukwandu Hanan Hindy Elochukwu Ukwandu
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.