Towards the Interpretation of Convolutional Neural Networks for Image Classification Using Fuzzy Sets

Convolutional Neural Networks (CNNs) have demonstrated an outstanding performance on a range of image classification problems in various domains. However, their major drawback is that they are “black box” and opaque classifiers. Taking into consideration the increasing demand for interpretable classification models, this paper introduces a novel meta-feature extraction scheme. This scheme is based on fuzzy sets, and it can be applied on the feature maps of a CNN. Initially, representative image prototypes are selected based on their deep feature map representation. Then, it constructs information granules from the feature maps, describing the content of each image class. It uses fuzzy sets to linguistically characterize the similarity between the deep feature maps of an image and the deep feature maps of the image prototypes. Thus, a classification outcome can be interpreted based on the features characterizing the different image classes involved in a classification problem. The experimental evaluation of the proposed scheme is performed on five publicly available image datasets. The results indicate that the proposed scheme outperforms other state-of-the-art classifiers, while providing an understandable interpretation of the classification result.