Affiliations: [a]
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| [b]
University of Education Winneba, Kumasi-Campus, Ghana
| [c]
College of Internet of Things Engineering, Hohai University, China
| [d]
School of Information Science and Technology, Tibet University, Lhasa, China
Correspondence:
[*]
Corresponding author. Yongbin Yu, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China. E-mail: [email protected].
Abstract: The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time.
