A pneumonia classification accuracy enhancement scheme based on data augmentation, data preprocessing, and transfer learning

Tianfu Mao; Teng Li; Yu Zhang

doi:10.1117/12.2674701

23 May 2023 A pneumonia classification accuracy enhancement scheme based on data augmentation, data preprocessing, and transfer learning

Tianfu Mao, Teng Li, Yu Zhang

Author Affiliations +

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126041H (2023) https://doi.org/10.1117/12.2674701
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

As an infectious disease, pneumonia can cause great harm to human health. If pneumonia can be detected and treated early, its harm will be greatly reduced. Previously, hospitals relied on specialized doctors to diagnose diseases, but with advances in computer technology, deep learning is widely used in the medical field. In recent years, many excellent pneumonia classification methods have been proposed. They can judge whether a patient is infected with pneumonia based on their chest x-ray image, which effectively solves the shortage of professional doctors. In this paper, a convolutional neural network was proposed for pneumonia classification, and the pneumonia classification model was trained based on 1211 real chest x-ray image provided by Third Military Medical University. Experimental results on the test set show that the convolutional neural network proposed in this paper is not dominant, and its classification accuracy is only 72.0%, which is lower than the other three pneumonia classification models compared. Therefore, this paper integrates data enhancement, data preprocessing, transfer learning technology, then proposes a pneumonia classification accuracy enhancement scheme. This scheme improves the classification accuracy of the proposed pneumonia classification model from 72.0% to 82.0%, and the classification effect exceeds the other three pneumonia classification models compared.

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Tianfu Mao, Teng Li, and Yu Zhang "A pneumonia classification accuracy enhancement scheme based on data augmentation, data preprocessing, and transfer learning", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126041H (23 May 2023); https://doi.org/10.1117/12.2674701

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KEYWORDS

Education and training

Chest imaging

Deep learning

Convolutional neural networks

Data modeling

Machine learning

Image classification

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