Image recognition on CT image for COVID-19 detection

Hanyi Wang

doi:10.1117/12.2641204

19 October 2022 Image recognition on CT image for COVID-19 detection

Hanyi Wang

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122944E (2022) https://doi.org/10.1117/12.2641204
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

Image recognition has always been a popular question in computer vision, with numerous works proposed. With the prevailing pandemic of COVID since 2019, there is an increasing demand for detecting COVID from CT images. In this paper, we aim to implement several prevailing image recognition models on CT images and compare their performance. The methods used include the AlexNet, the VGG net, and the SENet. Experiments on the open source COVID x CT dataset show that SENet model has the best performance, obtaining a precision of 85.07%. We validate our method through numerous experiments. We hope our method can achieve the automatic detection for COVID-19 pandemic.

Citation Download Citation

Hanyi Wang "Image recognition on CT image for COVID-19 detection", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122944E (19 October 2022); https://doi.org/10.1117/12.2641204

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KEYWORDS

Computed tomography

Detection and tracking algorithms

Data processing

Image processing

Performance modeling

Convolution

Data modeling

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