CViTLNN: A Hybrid Approach based on Vision Transformer and Liquid Neural Network for COVID-19 Detection
Received: 26 February 2025 | Revised: 23 March 2025 | Accepted: 2 April 2025 | Online: 20 April 2025
Corresponding author: Muhammad Waqas
Abstract
The COVID-19 pandemic has underscored the need for accurate and rapid diagnostic tools to assist clinical decision-making. Conventional deep learning models for COVID-19 detection in Chest X-Ray (CXR) images face challenges in poor generalization across imaging conditions and high computational demands. To address these issues, this study proposes CViTLNN, a novel hybrid model combining Vision Transformers (ViTs) and Liquid Neural Networks (LNNs) to improve feature extraction and classification. Specifically, CViTLNN employs a ViT with 24 transformer encoder blocks for efficient extraction of spatial features. The self-attention mechanism of ViTs effectively captures global and local dependencies in CXR images. Furthermore, it incorporates a four-layer LNN for dynamic refinement of features for decision-making. Experimental results demonstrate a test accuracy of 94%, a precision of 95%, and a recall of 94% on a COVID dataset of 5228 CXRs, minimizing false negatives and ensuring high sensitivity. The proposed model provides an efficient and scalable AI-driven diagnostic solution, making it highly suitable for real-world clinical applications, especially in resource-constrained settings.
Keywords:
COVID-19 detection, Vision Transformers (ViTs), Liquid Neural Network (LNN), chest X-ray analysis, medical imagingDownloads
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Copyright (c) 2025 Muhammad Waqas, Florentin Smarandache, Muhammad Yasir, Farrukh Arslan, Anum Ali
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