COV-TViT: An Improved Diagnostic System for COVID Pneumonitis Utilizing Transfer Learning and Vision Transformer on X-Ray Images

Sunil Kumar; Amar Pal  Yadav; Neha  Nandal; Vishal  Awasthi; Luxmi  Sapra; Prachi  Chhabra

doi:10.35882/jeeemi.v7i4.1037

Sunil Kumar School of Engineering and Technology (UIET), CSJM University, Kanpur, India https://orcid.org/0000-0003-1103-513X
Amar Pal Yadav Department of Computer Science and Engineering, Noida Institute of Engineering and Technology, Greater Noida, India https://orcid.org/0009-0006-9070-0167
Neha Nandal Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad, India https://orcid.org/0000-0003-2566-5925
Vishal Awasthi School of Engineering and Technology (UIET), CSJM University, Kanpur, India https://orcid.org/0009-0009-9511-6252
Luxmi Sapra School of Computing, Graphic Era Hill University, Dehradun, India https://orcid.org/0000-0002-8398-5030
Prachi Chhabra Department of Information Technology, JSS Academy of Technical Education Noida, Noida, India https://orcid.org/0000-0002-1743-6703

DOI: https://doi.org/10.35882/jeeemi.v7i4.1037

Keywords: Convolution Neural Network, Deep Learning, Machine Learning, Self-Attention, VGG-16, ViT.

Abstract

COVID is a contagious lung ailment that continues to be a world curse, and it remains a highly infectious respiratory disease with global health implications. Traditional diagnostic methods, such as RT-PCR, though widely used, are often constrained by high costs, limited accessibility, and delayed results. In contrast, radiology for lung disease detection has been proven advantageous for identifying deformities, and chest X-rays are the most preferred radiological method due to their non-invasive nature. To address these limitations, this study aims to develop an efficient, automated diagnostic system leveraging radiological imaging, specifically X-rays, which are cost-effective and widely available. The primary contribution of this research is the introduction of COV-TViT, a novel deep learning framework that integrates transfer learning with Vision Transformer (ViT) architecture for the accurate detection of COVID pneumonitis. The proposed method is evaluated using the COVID-QU-Ex dataset, which comprises a balanced set of X-ray images from COVID positive and healthy individuals. Methodologically, the system employs pre-trained convolutional neural networks (CNNs), specifically VGG16 and VGG19 (Visual Geometry Group), for transfer learning, followed by fine tuning to enhance feature extraction. The ViT model, known for its self-attention mechanism, is then applied to capture complex spatial dependencies in the X-ray images, enabling robust classification. Experimental results demonstrate that COV-TViT achieves a classification accuracy of 98.96% and an F1 score of 96.21%, outperforming traditional CNN based transfer learning models in several scenarios. These findings underscore the model’s potential for high-precision COVID pneumonitis detection. The proposed approach significantly transforms classification tasks using self-attention mechanisms to extract features and learn representations. Overall, the proposed diagnostic system COV-TViT can be advantageous in the fundamental identification of COVID pneumonitis.

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