Design and Evaluation of a New Capsule Neural Network (CapsNet) for Imbalanced Images Classification

Imbalanced image classification is one of the most important and difficult issues in data mining. With the inability of standard classification algorithms, Capsule neural networks (CapsNet) provide a good platform for designing imbalanced classification models by considering spatial communication of features, compared to other deep networks such as Convolutional Neural Networks (CNN). On the other hand, crack bifurcation in the surface cracks is one of the anomalies and minority categories in concrete structures that can be effective in the maintenance of concrete structures and cost management. Also, the surface crack image sets are suitable data for evaluating imbalanced classification due to their characteristics. Therefore, in this paper, a new architecture based on CapsNet is introduced to evaluate the imbalanced classification of surface crack images in the concrete structures. Examination and comparison of the proposed network with CNN in balanced and imbalanced image classification of surface cracks on 13,500 sets of collected images showed the superiority of the proposed network. Also, the proposed network showed a significant advantage compared to CNN in investigating the effect of reducing the number of training images on classification accuracy. This network performed balanced classification of surface cracks with 99.56% accuracy. Also, the proposed network has an accuracy of 80% up to the imbalance of theminority group to the 1:8 minority, which is very suitable compared to CNN.