@Article{jiot.2020.09797,
AUTHOR = {Shuqiao Liu, Junliang Li, Xiaojie Li},
TITLE = {Brain MRI Patient Identification Based on Capsule Network},
JOURNAL = {Journal on Internet of Things},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {135--144},
URL = {http://www.techscience.com/jiot/v2n4/40247},
ISSN = {2579-0080},
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.},
DOI = {10.32604/jiot.2020.09797}
}