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

• URL: http://arxiv.org/abs/2303.11935v1
• Date: Sat, 18 Mar 2023 12:38:23 GMT
• Title: Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images
• Authors: Bouthaina Slika, Fadi Dornaika, Hamid Merdji, Karim Hammoudi
• Abstract summary: We present a Vision Transformer-based neural network model that relies on a small number of trainable parameters to quantify the severity of COVID-19 and other lung diseases. Our model can provide peak performance in quantifying severity with high generalizability at a relatively low computational cost.
• Score: 11.12596879975844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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