Comparison of Convolutional Neural Network and Capsule Network in Image Recognition- A Case Study of Microbiopsy Images of Breast Tumor

Abstract

Cancer is now the leading causes of death. During treatment, if an abnormal mass is detected by routine mammography after histological examination, a biopsy will be performed for further diagnosis. However, the time required to review and evaluate the biopsy slide is very long. An effective algorithm that can identify cancer tissues and reduce the rate of misdiagnosis is urgently needed. The current research methods mainly include: Capsule Network, and Deep Convolutional Neural Networks (CNNs) used in image classification, etc. This paper mainly uses Capsule Network, CNN, CNN trained on Histogram of Oriented Gradient (HOG) images, and Grey Level Co-occurrence Matrix (GLCM) to classify benign and malignant images of the tumor micro-biopsy images. The first step is to download the breast tumor micro-biopsy images from Breakhis. The second step is to preprocess the images, mainly image enhancement, augmentation, and HOG feature extraction. In the third step, the processed image data is used to train CNN and Capsule Network. The last step is to visualize the accuracy of model training and use confusion matrix to calculate the accuracy of image classification, recall rate, and so on. The results show that the accuracy of CNN with the help of transfer learning can reach 98.32%, the accuracy of CNN trained with HOG image is only 82.99%, the accuracy of CNN trained with GLCM is 92.79%, and the accuracy of capsule network can reach 92.39%.