基于连体神经网络的 COVID-19 胸部 X 射线诊断方法

Engin Tas, Ayca Hatice Atli
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摘要

放射学检查结果在诊断 Covid-19、评估其严重程度和管理患者方面发挥着重要的辅助作用。基于医学影像的人工智能技术已在许多领域得到应用,并取得了令人振奋的发展,在抗击 Covid-19 大流行的斗争中,该技术已成为快速准确检测疾病的一个关注领域。其主要困难在于无法获得神经网络需要的具有高质量和标准图像的大型数据集。针对这一问题,本研究提出了一种基于连体神经网络的深度学习框架,用于利用胸部 X 光(CXR)图像准确诊断 Covid-19。基于迁移学习方法的预训练 VGG16 架构构成了连体神经网络的骨干。骨干层的输出通过合并层连接在一起,然后输出通过全连接层。基于这种结构,可为每个类别生成基于连体神经网络的类别感知模型。模型的预测结果通过投票机制进行组合,以减少误分类的可能性并做出更好的决策。该框架利用公开数据集对 Covid-19 肺炎、肺不张、正常和非 Covid-19 病毒性肺炎图像的 4 类分类任务进行了评估。结果表明,该框架的分辨能力很强,每类只用 10 张图像进行训练,训练时间更短,平均测试准确率达到 92%。我们的框架为每个类别学习一个基于连体的配对模型,能有效捕捉特定类别的特征。此外,它还能解决多类分类任务中数据稀缺和训练时间长的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Siamese neural network-based diagnosis of COVID-19 using chest X-rays

A Siamese neural network-based diagnosis of COVID-19 using chest X-rays

Radiological findings play an essential and complementary role in diagnosing Covid-19, assessing its severity, and managing its patients. Artificial intelligence technology based on medical imaging, which has made exciting developments by being applied in many areas, has become an area of interest for the rapid and accurate detection of the disease in the fight against the Covid-19 pandemic. The main difficulty is the inability to obtain a large dataset size with quality and standard images that neural networks need to perform well. Aiming at this problem, this study proposes a Siamese neural network-based deep learning framework for accurate diagnostics of Covid-19 using chest X-ray (CXR) images. The pre-trained VGG16 architecture, based on the transfer learning approach, forms the backbone of the Siamese neural network. The outputs of the backbones are joined together by a merging layer, and then the output passes through a fully connected layer. Based on this structure, category-aware Siamese-based models are produced for each class. The predictions from the models are combined using a voting mechanism to reduce the possibility of misclassification and to make better decisions. The framework was evaluated using a publicly available dataset for the 4-class classification task for Covid-19 pneumonia, lung opacity, normal, and non-Covid-19 viral pneumonia images. The findings reveal the high discrimination ability of the framework, trained using only 10 images per class in less training time, achieving an average test accuracy of 92%. Our framework, which learns a single Siamese-based pairwise model for each class, effectively captures class-specific features. Additionally, it has the potential to deal with data scarcity and long training time problems in multi-class classification tasks.

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