Comparative Analysis of State-of-the-Art Deep Learning Models for
Detecting COVID-19 Lung Infection from Chest X-Ray Images
Abstract
The ongoing COVID-19 pandemic has already taken millions of lives and
damaged economies across the globe. Most COVID-19 deaths and economic
losses are reported from densely crowded cities. It is comprehensible
that the effective control and prevention of epidemic/pandemic
infectious diseases is vital. According to WHO, testing and diagnosis is
the best strategy to control pandemics. Scientists worldwide are
attempting to develop various innovative and cost-efficient methods to
speed up the testing process. This paper comprehensively evaluates the
applicability of the recent top ten state-of-the-art Deep Convolutional
Neural Networks (CNNs) for automatically detecting COVID-19 infection
using chest X-ray images. Moreover, it provides a comparative analysis
of these models in terms of accuracy. This study identifies the
effective methodologies to control and prevent infectious respiratory
diseases. Our trained models have demonstrated outstanding results in
classifying the COVID-19 infected chest x-rays. In particular, our
trained models MobileNet, EfficentNet, and InceptionV3 achieved a
classification average accuracy of 95%, 95%, and 94% test set for
COVID-19 class classification, respectively. Thus, it can be beneficial
for clinical practitioners and radiologists to speed up the testing,
detection, and follow-up of COVID-19 cases.