Deep Cellular Automata-Based Feature Extraction for Classification of the Breast Cancer Image

Feature extraction is an important step in classification. It directly results in an improvement of classification performance. Recent successes of convolutional neural networks (CNN) have revolutionized image classification in computer vision. The outstanding convolution layer of CNN performs feature extraction to obtain promising features from images. However, it faces the overfitting problem and computational complexity due to the complicated structure of the convolution layer and deep computation. Therefore, this research problem is challenging. This paper proposes a novel deep feature extraction method based on a cellular automata (CA) model for image classification. It is established on the basis of a deep learning approach and multilayer CA with two main processes. Firstly, in the feature extraction process, multilayer CA with rules are built as the deep feature extraction model based on CA theory. The model aims at extracting multilayer features, called feature matrices, from images. Then, these feature matrices are used to generate score matrices for the deep feature model trained by the CA rules. Secondly, in the decision process, the score matrices are flattened and fed into the fully connected layer of an artificial neural network (ANN) for classification. For performance evaluation, the proposed method is empirically tested on BreaKHis, a popular public breast cancer image dataset used in several promising and popular studies, in comparison with the state-of-the-art methods. The experimental results show that the proposed method achieves the better results up to 7.95% improvement on average when compared with the state-of-the-art methods.