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Automatic classification of medical X-ray images: hybrid generative-discriminative approach

机译：医学X射线图像的自动分类：混合生成-判别方法

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摘要

A new approach is presented to improve the classification performance of medical X-ray images based on the combination of generative and discriminative classification approach. A set of labelled X-ray images were given from 116 categories of different parts of body and the aim is to construct a classification model. This model was then used to classify any new X-ray images into one of the predefined categories. The classification task started with extracting local invariant features from all images. A generative model such as probabilistic latent semantic analysis (PLSA) was applied on extracted features in order to provide more stable representation of the images. Subsequently, this representation was used as input to a discriminative support vector machine classifier to construct a classification model. The experimental results were based on ImageCLEF 2007 medical database. The classification performance was evaluated on the entire dataset as well as the class specific level. It was also compared with other classification techniques with various image representations on the same database. The comparison results showed that superior performance has been achieved especially for classes with less number of training images. Thus, only 7 out of 116 classes were left with accuracy rate below 60% as it differs from the results obtained using other classification approaches. This was attained by exploiting the ability of PLSA to generate a better image representation, discriminative for accurate classification and more robust when less training data are available. The total classification rate obtained on the entire dataset is 92.5%.
机译：提出了一种基于生成和判别分类方法相结合的改进医学X射线图像分类性能的新方法。从身体不同部位的116个类别中给出了一组标记的X射线图像，目的是构建分类模型。然后使用该模型将任何新的X射线图像分类为预定义的类别之一。分类任务首先从所有图像中提取局部不变特征。生成模型（如概率潜在语义分析（PLSA））应用于提取的特征，以便提供图像的更稳定表示。随后，将该表示用作判别支持向量机分类器的输入，以构建分类模型。实验结果基于ImageCLEF 2007医学数据库。在整个数据集以及班级特定级别上评估了分类性能。还将它与其他分类技术（在同一数据库上具有各种图像表示形式）进行了比较。比较结果表明，特别是对于训练图像数量较少的班级，已经实现了卓越的性能。因此，在116个类别中，只有7个类别的准确率低于60％，这与使用其他分类方法获得的结果不同。这是通过利用PLSA生成更好的图像表示，区分准确分类的能力以及在可提供较少训练数据时更强大的能力来实现的。在整个数据集上获得的总分类率为92.5％。

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