Research on chest x-ray image diagnosis of COVID-19 based on improved ResNet

Jiangzihan Sun

doi:10.1117/12.2667614

22 February 2023 Research on chest x-ray image diagnosis of COVID-19 based on improved ResNet

Jiangzihan Sun

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125870Z (2023) https://doi.org/10.1117/12.2667614
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19.

Citation Download Citation

Jiangzihan Sun "Research on chest x-ray image diagnosis of COVID-19 based on improved ResNet", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125870Z (22 February 2023); https://doi.org/10.1117/12.2667614

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KEYWORDS

Convolution

COVID 19

Chest

Education and training

Deep learning

Computed tomography

Feature extraction

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