基于密集连接神经网络的肺部X光多分类方法

Lung X-Ray Multi-Label Classification Method Which Is Based on Densely Connected Neural Network

摘要: 近几年，神经网络在医学病种分类方面经历着迅速的发展，在某些病种上面的分类水平已经达到甚至超过专业医学从业人员的水准。但是神经网络在医学上的应用主要集中在病种的二分类方面，在病种的多分类方面神经网络模型的表现还不尽人意。概括的说，神经网络特征提取的不完美、医学图像的复杂性以及相关领域专业知识的匮乏是导致该结果的三个主要因素。为此，本文提出了一种基于神经网络密集连接并且包含置信度概念的多分类方法。该方法能够有效融合病种的多维度特征并且使用置信度概念保证分类结果的可靠性。本文在CXR14数据集上进行测试，实验表明，该方法能够有效融合不同维度的特征，从而增强了特征提取的能力；引入置信度概念的分类方法应用于病种标签树能够获取达到预期水平的分类可信度，而不是像目前的二分类只能提供绝对的分类结果。这为临床医疗诊断提供了更加丰富和可靠的病种分类信息。

Abstract: In recent years, neural networks have experienced rapid development in the medical diseases classification. The classification ability of certain diseases has reached or even exceeded the level of professional medical practitioners. However, the application of these neural networks is mainly focused on the binary classification of diseases, and the performance of neural network models in the multi-label classification of diseases is not satisfactory. The imperfection of feature extraction in the neural network, the complexity of medical images, and the lack of expertise in related fields are the three main factors leading to this result. To this end, this paper proposes a confidence multi-label classification method based on dense neural network connections, which can effectively fuse the multi-dimensional features of the disease and use the concept of confidence to ensure the reliability of the classification results. This article is tested on the CXR14 datasets. Experiments show that the method in this article can effectively integrate features of different dimensions to enhance the ability of feature extraction. Based on the concept of confidence, we can obtain the expected level of classification credibility instead of the current definite classification that can only provide absolute classification results. This provides more abundant and reliable identification information for clinical medical diagnosis.