Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916452328538112 |
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| author | Slika, Bouthaina Dornaika, Fadi Merdji, Hamid Hammoudi, Karim |
| author_facet | Slika, Bouthaina Dornaika, Fadi Merdji, Hamid Hammoudi, Karim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_11935 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images Slika, Bouthaina Dornaika, Fadi Merdji, Hamid Hammoudi, Karim Image and Video Processing Computer Vision and Pattern Recognition Machine Learning I.4.0; I.4.9; I.4.7; I.2.0 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. |
| title | Vision Transformer-based Model for Severity Quantification of Lung Pneumonia Using Chest X-ray Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning I.4.0; I.4.9; I.4.7; I.2.0 |
| url | https://arxiv.org/abs/2303.11935 |