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To develop generic and reliable approaches for diagnosing and assessing theseverity of COVID-19 from chest X-rays (CXR), a large number of well-maintainedCOVID-19 datasets are needed. Existing severity quantification architecturesrequire expensive training calculations to achieve the best results. Forhealthcare professionals to quickly and automatically identify COVID-19patients and predict associated severity indicators, computer utilities areneeded. In this work, we propose a Vision Transformer (ViT)-based neuralnetwork model that relies on a small number of trainable parameters to quantifythe severity of COVID-19 and other lung diseases. We present a feasibleapproach to quantify the severity of CXR, called Vision Transformer RegressorInfection Prediction (ViTReg-IP), derived from a ViT and a regression head. Weinvestigate the generalization potential of our model using a variety ofadditional test chest radiograph datasets from different open sources. In thiscontext, we performed a comparative study with several competing deep learninganalysis methods. The experimental results show that our model can provide peakperformance in quantifying severity with high generalizability at a relativelylow computational cost. The source codes used in our work are publiclyavailable at https://github.com/bouthainas/ViTReg-IP.

Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images