The effectiveness of image augmentation in pneumonia diagnosis using convolutional neural network

Yingzhi Tao

doi:10.1117/12.2673628

14 April 2023 The effectiveness of image augmentation in pneumonia diagnosis using convolutional neural network

Yingzhi Tao

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130D (2023) https://doi.org/10.1117/12.2673628
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

In human history, pneumonia has resulted several disasters, including the COVID-19. How to diagnosis a pneumonia timely and efficiently becomes a problem. A Computer-Aided Diagnosis (CAD) system with classification algorithms based on convolutional neural network are considered to be the most potential methods. But lack of data to train the network is a great challenge in its popularity. To help relieve the phenomenon, this paper is to explore some traditional methods in data augmentation to let a Convolutional Neural Network (CNN) trained well with less data. In this paper, two different models are applied, and both will employ five datasets with different way of data augmentation. The five datasets include the original one, the one with oversampling, the one with geometric augmentation, the one with color space transformation and the one with both geometric augmentation and color spacer transformation (it will be called the mixed method in the paper). The results show that oversampling has a limited improvement in accuracy, geometric augmentation has a great effect in the accuracy, color space transformation has a bad result, the mixed method performs as awful as color space transformation does and the second model with batch normalization perform well in all five datasets. Finally, an 80.34% accuracy is achieved. It can be concluded that, oversampling can balance the dataset and provide a limited increase, geometric augmentation is a good strategy while color space transformation is not and model with batch normalization can exclude the influence of extreme data and perform well and stable.

Citation Download Citation

Yingzhi Tao "The effectiveness of image augmentation in pneumonia diagnosis using convolutional neural network", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130D (14 April 2023); https://doi.org/10.1117/12.2673628

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KEYWORDS

Convolutional neural networks

Deep learning

Batch normalization

Image processing

Classification systems

Data processing

Image classification

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