Enhanced Capsule Network for Medical image classification.

Abstract

Nowadays, cancer has become a major threat to people's lives and health. Convolutional neural network (CNN) has been used for cancer early identification, which cannot achieve the desired results in some cases, such as images with affine transformation. Due to robustness to rotation and affine transformation, capsule network can effectively solve this problem of CNN and achieve the expected performance with less training data, which are very important for medical image analysis. In this paper, an enhanced capsule network is proposed for medical image classification. For the proposed capsule network, the feature decomposition module and multi-scale feature extraction module are introduced into the basic capsule network. The feature decomposition module is presented to extract richer features, which reduces the amount of calculation and speeds up the network convergence. The multi-scale feature extraction module is used to extract important information in the low-level capsules, which guarantees the extracted features to be transmitted to the high-level capsules. The proposed capsule network was applied on PatchCamelyon (PCam) dataset. Experimental results show that it can obtain good performance for medical image classification task, which provides good inspiration for other image classification tasks.