This paper proposes a novel classification method. Firstly, we use the deep neural network (DNN) to classify the training set. After several iterations, we obtain the output vector Y. The component of the largest value in vector Y is represented as the label being classified, which we take as the output value. Because we chose the sigmoid function as our activation function, the output value is between 0 and 1. Therefore, the output value can represents the probability of the classified label by the DNN. Depending on the distribution of output values, we set tolerance values (Tol) that categorize similar output values as the same label in the DNN. If the output value is lower than Tol, we consider it categorically anomalous. Subsequently, we use the Phase-Field model to classify these anomalies and obtain better classification results. As this classification method combines Phase-Field model and DNN, we named it Phase-Field-DNN. In the numerical experiment using MNIST handwritten digit data set as experimental data, the classification accuracy of Phase-Field-DNN model is higher than that of Phase-Field model and DNN model through the analysis of the classification results of binary classification and multi-classification problems with this data. In addition, the model we proposed is used to classify the normal and abnormal brain MRIs, and the classification results are compared with those of others. After comparison, we find that our proposed model achieve the best classification results.
Bibliographical noteFunding Information:
The first author Jian Wang expresses thanks for the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant nos. 22KJB110020). The corresponding author (J.S. Kim) was supported by Korea University Grant. The authors would like to thank the reviewers for their comments and suggestions.
© 2023 Elsevier Ltd
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence