Patch-and-amplify Capsule Network for the recognition of gastrointestinal diseases

Abstract

Deep learning (DL) algorithms require massive amounts of data in diverse variations to attain close to human recognition accuracy. This enables them to perform well on unseen in-distribution viewpoints. However, critical areas such as health face a challenge in generating large data. This is mainly attributed to regulatory policies surrounding the collection of such data. Hence, the domain of health is known to have smaller datasets. This limitation, therefore, requires algorithms that can derive maximum benefit from the small-size dataset. An example of such an algorithm is Capsule Network (CapsNet). Though Capsule Networks perform well on smaller datasets, they attain low performance on datasets consisting of complex real-life images. This challenge is attributed to their weak encoders. This paper, therefore, proposes a feature enhancement technique termed patch-and-amplify to improve the feature extraction task and enhance extracted features in the encoder layer. A model-specific squash function is proposed to enhance the coupling of capsules between the higher and lower capsule layers and reduce the impact of polarization. To evaluate the network's robustness and generalizability, the proposed model was evaluated on CIFAR-10, Fashion-MNIST, and Kvasir-V2 datasets achieving recognition accuracies of 85.50 %, 98.40 %, and 93.40 %. Extensive visualizations were explored to assist in understanding the predictions and internal structure of the network. The proposed model performs well compared with the state-of-the-art in the literature and can serve as a helpful tool in assisting medical practitioners in diagnosing gastrointestinal diseases.

Keywords

• Capsule network
• Feature enhancement
• Gastrointestinal Diseases