Abstract
This research focuses on developing a practical approach for rapid and accurate COVID-19 detection from lung ultrasound images. We leverage attention models and image pre-processing techniques to enhance the detection process. Deep learning models, including VGG16, VGG19, and ResNet18, are utilized as backbone architectures. The images undergo pre-processing, including denoising, normalization, and contrast enhancement, to improve relevant features and reduce noise.
Additionally, we integrate the Convolutional Block Attention Module (CBAM) to capture informative regions of interest. Experimental evaluations on a dataset containing COVID-19-infected, pneumonia-infected, and healthy lungs demonstrate improved accuracy, sensitivity, and specificity compared to traditional methods. The combined use of attention models and image pre-processing techniques enhances COVID-19 detection from lung ultrasound images, with the CBAM module effectively highlighting significant regions for accurate classification.
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Acknowledgment
Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number P20GM104420. The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health.
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Le, H.T., Nguyen, L., Nguyen, T.D. (2023). Attention Models and Image Pre-processing for Covid-19 Detection Based on Lung Ultrasound Images. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_33
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DOI: https://doi.org/10.1007/978-981-99-8296-7_33
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