Three-Way Image Classification with Evidential Deep Convolutional Neural Networks

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.

Automated summary

In this study, a three-way image classification method based on deep convolutional neural networks and evidence theory is proposed, which is validated to effectively identify uncertain images and reduce risks in image classification.