Open Access

ARTICLE

Brain MRI Patient Identification Based on Capsule Network

Shuqiao Liu, Junliang Li, Xiaojie Li^*

Chengdu University of Information Technology, Chengdu, 610225, China

* Corresponding Author: Xiaojie Li. Email:

Journal on Internet of Things 2020, 2(4), 135-144. https://doi.org/10.32604/jiot.2020.09797

Received 20 May 2020; Accepted 15 August 2020; Issue published 22 September 2020

Abstract

In the deep learning field, “Capsule” structure aims to overcome the shortcomings of traditional Convolutional Neural Networks (CNN) which are difficult to mine the relationship between sibling features. Capsule Net (CapsNet) is a new type of classification network structure with “Capsule” as network elements. It uses the “Squashing” algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classification performance. The main problem of the Brain Magnetic Resonance Imaging (Brain MRI) recognition algorithm is that the difference between Alzheimer’s disease (AD) image, the Mild Cognitive Impairment (MCI) image, and the normal image is not significant. It is difficult to achieve excellent results using a multi-layer CNN. However, CapsNet can be in the case of a shallower network, which can accommodate more useful feature information for identifying brain MRI. In this paper, we designed a shallow CapsNet to identify patients with brain MRI by binary classification. Compared with VGG16, Resnet34, DenseNet121 and ResNeXt50. Experimental results illustrate that CapsNet is superior to CNN network in its accuracy and F1-score. The indicators were 86.67% and 83.33%, respectively. Furthermore, we show that the capsule network shows excellent performance in brain MRI recognition compared with those popular networks.

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Keywords

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