  To develop generic and reliable approaches for diagnosing and assessing the
severity of COVID-19 from chest X-rays (CXR), a large number of well-maintained
COVID-19 datasets are needed. Existing severity quantification architectures
require expensive training calculations to achieve the best results. For
healthcare professionals to quickly and automatically identify COVID-19
patients and predict associated severity indicators, computer utilities are
needed. In this work, we propose a Vision Transformer (ViT)-based neural
network model that relies on a small number of trainable parameters to quantify
the severity of COVID-19 and other lung diseases. We present a feasible
approach to quantify the severity of CXR, called Vision Transformer Regressor
Infection Prediction (ViTReg-IP), derived from a ViT and a regression head. We
investigate the generalization potential of our model using a variety of
additional test chest radiograph datasets from different open sources. In this
context, we performed a comparative study with several competing deep learning
analysis methods. The experimental results show that our model can provide peak
performance in quantifying severity with high generalizability at a relatively
low computational cost. The source codes used in our work are publicly
available at https://github.com/bouthainas/ViTReg-IP.
