scholarly journals Simulation study of deep-learning-based gait classification of young/elderly adults using Doppler radar

2021 ◽  
Vol 18 ◽  
pp. 100103
Author(s):  
Toshiyuki Hoshiga ◽  
Kenshi Saho ◽  
Keitaro Shioiri ◽  
Masahiro Fujimoto ◽  
Yoshiyuki Kobayashi
Keyword(s):  
Deep Learning ◽  
Simulation Study ◽  
Doppler Radar ◽  
Elderly Adults ◽  
Gait Classification

Gait Classification of Young Adults, Elderly Non-Fallers, and Elderly Fallers Using Micro-Doppler Radar Signals: Simulation Study

2017 ◽  
Vol 17 (8) ◽  
pp. 2320-2321 ◽  
Author(s):  
Kenshi Saho ◽  
Masahiro Fujimoto ◽  
Masao Masugi ◽  
Li-Shan Chou
Keyword(s):  
Young Adults ◽  
Simulation Study ◽  
Doppler Radar ◽  
Gait Classification ◽  
Radar Signals

Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology

2019 ◽  
Vol 7 (5) ◽  
pp. 188-191
Author(s):  
I.Gayathri Devi ◽  
G. Surya Kala Eswari ◽  
G. Kumari
Keyword(s):  
Deep Learning ◽  
Retail Market

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

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