The effect of data augmentation and depth of networks on CNN-based breast x-ray image diagnosis of pneumonia

Yiming Wang

doi:10.1117/12.2659346

22 November 2022 The effect of data augmentation and depth of networks on CNN-based breast x-ray image diagnosis of pneumonia

Yiming Wang

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Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124750K (2022) https://doi.org/10.1117/12.2659346
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

COVID-19 has become an epidemic that has a strong influence on people all over the world. Many methods, like nucleic acid test, are proposed to discover the infected as early as possible in order to prevent further infection. However, as all-involvement nucleic acid test becomes more and more frequent in China, the quality of testing cannot be guaranteed, and the efficiency is quite low. Moreover, there is still a period of time from one who is infected takes a test to he or she is found infected and separated from others, which means that more people are prone to being infected during this period. Furthermore, false positive and false negative problems are worth attentive consideration, and the consequences are inestimable. [1] show that, in a population with a low prevalence of COVID-19 cases, even a highly sensitive and specific test returns many false-positive results.

Citation Download Citation

Yiming Wang "The effect of data augmentation and depth of networks on CNN-based breast x-ray image diagnosis of pneumonia", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124750K (22 November 2022); https://doi.org/10.1117/12.2659346

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KEYWORDS

Data modeling

X-ray imaging

X-rays

Breast

Image classification

Performance modeling

Visual process modeling

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