The application of deep learning for COVID-19 diagnosis and treatment

Jiahua Li

doi:10.1117/12.2669642

24 March 2023 The application of deep learning for COVID-19 diagnosis and treatment

Jiahua Li

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126110M (2023) https://doi.org/10.1117/12.2669642
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has resulted in a considerable increase in hospitalizations, leading to an increasing demand for accurate and efficient techniques for diagnosis. The CT-based diagnosis can provide pathologic information to assist treatment but be restricted due to inefficient and relatively complicated implementation. With the advent of deep learning and advanced hardware, an AI-assisted method diagnosis and segmentation for COVID-19 are proposed. In this paper, many traditional machine learning methods for imaging classification and segmentation are discussed, such as k-Nearest Neighbours (KNN), support vector machines (SVM), edge-based or region-based segmentation. In addition, we proposed a ResNet-based model and an improved U-Net for medical tasks of classification and segmentation, respectively. Our proposed model achieved desirable accuracy in medical applications.

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Jiahua Li "The application of deep learning for COVID-19 diagnosis and treatment", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126110M (24 March 2023); https://doi.org/10.1117/12.2669642

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KEYWORDS

Image segmentation

COVID 19

Deep learning

Biomedical applications

Computed tomography

Convolution

Lung

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