COVID-19 diagnosis with convolution neural networks using CT images

Yiwen Xiang

doi:10.1117/12.2628166

22 April 2022 COVID-19 diagnosis with convolution neural networks using CT images

Yiwen Xiang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 1216347 (2022) https://doi.org/10.1117/12.2628166
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

As COVID-19 has spread worldwide, detecting the patients of COVID-19 and taking effective actions has gained more and more importance. Applying a deep learning framework to detect medical pictures has already been used for years. This paper mainly trained a large number of CT images of patients and normal people on three networks: AlexNet, VGG, and ResNet. Based on PyTorch, we build the network successfully and soon examine the performance of the three networks on the test and validation dataset. Our experiments demonstrate that the ResNet performs the best when detecting the COVID-19 CT images. It reaches the accuracy of 99.5%, which proves that it has a strong fitting ability in our dataset, which is not so large. However, when applying the pre-trained model from the bigger dataset in a smaller dataset, the accuracy of AlexNet and VGGNet will increase accordingly while the accuracy of ResNet decreases. Though we have made many assumptions about the phenomenon, more experiments are needed after the experiment.

Citation Download Citation

Yiwen Xiang "COVID-19 diagnosis with convolution neural networks using CT images", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 1216347 (22 April 2022); https://doi.org/10.1117/12.2628166

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