基于胸部 X 光图像分类的深度学习模型比较

Yiqing Zhang, Yukun Xu, Zhengyang Kong, Zheqi Hu
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引用次数: 0

摘要

肺炎是以肺部炎症为特征的常见呼吸道疾病,因此准确诊断和及时治疗显得尤为重要。尽管医学图像分割技术取得了一些进展,但在实际应用中仍存在过拟合和低效率的问题。本文旨在利用图像数据增强方法来减轻过拟合,实现对 X 光图像中肺部感染的轻量级、高精度自动检测。我们使用增强和未增强图像数据集训练了三种模型,即 VGG16、MobileNetV2 和 InceptionV3。比较结果表明,增强型 VGG16 模型(VGG16-Augmentation)的平均准确率达到 96.8%。虽然 MobileNetV2-Augmentation 的准确率略低于 VGG16-Augmentation,但它仍然达到了 94.2% 的平均预测准确率,而且模型参数数量仅为 VGG16-augmentation 的 1/9。这尤其有利于肺炎患者的快速筛查和更高效的实时检测场景。通过这项研究,我们展示了图像数据增强方法在肺炎检测中的潜在应用,并对不同模型进行了性能比较。这些发现为肺炎患者的快速诊断和筛查提供了有价值的见解,并为未来的研究和在实际医疗环境中实施高效的肺部状况实时监测提供了有益的指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of deep learning models based on Chest X-ray image classification
Pneumonia is a common respiratory disease characterized by inflammation in the lungs, emphasizing the importance of accurate diagnosis and timely treatment. Despite some progress in medical image segmentation, overfitting and low efficiency have been observed in practical applications. This paper aims to leverage image data augmentation methods to mitigate overfitting and achieve lightweight and highly accurate automatic detection of lung infections in X-ray images. We trained three models, namely VGG16, MobileNetV2, and InceptionV3, using both augmented and unaugmented image datasets. Comparative results demonstrate that the augmented VGG16 model (VGG16-Augmentation) achieves an average accuracy of 96.8%. While the accuracy of MobileNetV2-Augmentation is slightly lower than that of VGG16-Augmentation, it still achieves an average prediction accuracy of 94.2% and the number of model parameters is only 1/9 of VGG16-augmentation. This is particularly beneficial for rapid screening of pneumonia patients and more efficient real-time detection scenarios. Through this study, we showcase the potential application of image data augmentation methods in pneumonia detection and provide performance comparisons among different models. These findings offer valuable insights for the rapid diagnosis and screening of pneumonia patients and provide useful guidance for future research and the implementation of efficient real-time monitoring of lung conditions in practical healthcare settings.
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