The farfetched certain classification of uncertain data suffers serious risks. Three-Way Decision (3WD) theory is utilized to implement uncertain data classification methods. Three-way uncertain data classification methods facilitate reducing decision risk and involving human–machine coordination through finding out uncertain cases for abstaining identification. Due to the limitation of traditional classifiers in feature learning, most existing three-way uncertain data classification methods are not good at handling the unstructural data of digital images. This shortage hinders the applications of three-way uncertain data classification in image-based decision support systems, such as the medical decision support systems based on radiographs. In this paper, we adopt deep convolutional neural networks (DCNNs) for feature learning and Dempster–Shafer (D-S) evidence theory as uncertainty measure to implement a three-way method for image classification. We utilize evidence theory to measure the uncertainty of the predictions produced by DCNNs and construct a novel evidential deep convolutional neural network (EviDCNN). Based on this, we propose a Three-Way Classification method with EviDCNN (EviDCNN-3WC). The experiments on massive medical image data sets validate that the proposed three-way classification method with EviDCNN is effective to identify uncertain images and reduce the risk in image classification. The superiorities of the proposed method facilitate its applications in image-based medical decision support systems.

不确定数据的某些分类牵强附会，存在严重的风险。三向决策（3WD）理论用于实现不确定的数据分类方法。三向不确定数据分类方法可通过找出不确定的案例以放弃识别，从而有助于降低决策风险并涉及人机协调。由于传统分类器在特征学习中的局限性，大多数现有的三向不确定数据分类方法都不能很好地处理数字图像的非结构化数据。这种短缺阻碍了三向不确定数据分类在基于图像的决策支持系统（例如基于射线照片的医学决策支持系统）中的应用。在本文中，我们采用深度卷积神经网络（DCNN）进行特征学习，并采用Dempster–Shafer（DS）证据理论作为不确定性度量，以实现图像分类的三向方法。我们利用证据理论来衡量DCNN产生的预测的不确定性，并构建一个新颖的证据深度卷积神经网络（EviDCNN）。基于此，我们提出了一种使用EviDCNN（EviDCNN-3WC）的三向分类方法。对大量医学图像数据集的实验证明，所提出的基于EviDCNN的三向分类方法可有效地识别不确定的图像并降低图像分类的风险。所提出的方法的优点促进了其在基于图像的医疗决策支持系统中的应用。