IEICE TRANSACTIONS on Information
Diabetes Noninvasive Recognition via Improved Capsule Network
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Cunlei WANG
Tianjin University,Tianjin Vocational College of Mechanics and Electricity
Donghui LI
Tianjin University
Keyword
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Cunlei WANG, Donghui LI, "Diabetes Noninvasive Recognition via Improved Capsule Network" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 8, pp. 1464-1471, August 2022, doi: 10.1587/transinf.2022EDP7037.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7037/_p
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@ARTICLE{e105-d_8_1464,
author={Cunlei WANG, Donghui LI, },
journal={IEICE TRANSACTIONS on Information},
title={Diabetes Noninvasive Recognition via Improved Capsule Network},
year={2022},
volume={E105-D},
number={8},
pages={1464-1471},
abstract={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.},
keywords={},
doi={10.1587/transinf.2022EDP7037},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Diabetes Noninvasive Recognition via Improved Capsule Network
T2 - IEICE TRANSACTIONS on Information
SP - 1464
EP - 1471
AU - Cunlei WANG
AU - Donghui LI
PY - 2022
DO - 10.1587/transinf.2022EDP7037
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2022
AB - 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.
ER -
English
