Diabetes Noninvasive Recognition via Improved Capsule Network

Cunlei WANG
Donghui LI

IEICE TRANSACTIONS on Information and Systems   Vol.E105-D    No.8    pp.1464-1471
Publication Date: 2022/08/01
Publicized: 2022/05/06
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2022EDP7037
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
diabetes noninvasive recognition,  Capsule Network,  plantar pressure image,  semantic fusion,  locality-constrained dynamic routing,  

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Noninvasive recognition is an important trend in diabetes recognition. Unfortunately, the accuracy obtained from the conventional noninvasive recognition methods is low. This paper proposes a novel Diabetes Noninvasive Recognition method via the plantar pressure image and improved Capsule Network (DNR-CapsNet). The input of the proposed method is a plantar pressure image, and the output is the recognition result: healthy or possibly diabetes. The ResNet18 is used as the backbone of the convolutional layers to convert pixel intensities to local features in the proposed DNR-CapsNet. Then, the PrimaryCaps layer, SecondaryCaps layer, and DiabetesCaps layer are developed to achieve the diabetes recognition. The semantic fusion and locality-constrained dynamic routing are also developed to further improve the recognition accuracy in our method. The experimental results indicate that the proposed method has a better performance on diabetes noninvasive recognition than the state-of-the-art methods.

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