The image-based analysis and classification of urine sediments using a LeNet-5 neural network-Reference-Cited by-同舟云学术

The image-based analysis and classification of urine sediments using a LeNet-5 neural network

Published:2019-05-02 Issue:1 Volume:8 Page:109-114
ISSN:2168-1163
Container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Author:

Li Taihao¹²,Jin Di³,Du Cuifen⁴,Cao Xiumei⁵,Chen Haige³,Yan Jianshe⁵⁶,Chen Na⁴,Chen Zhenyi⁴,Feng Zhenzhen⁴,Liu Shupeng⁴

Affiliation:

1. College of Medical Instruments, Shanghai University of Medicine & Health Sciences, Shanghai, China

2. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, P. R. China

3. Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P. R. China

4. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China

5. Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China

6. School of Life Sciences, Shanghai University, Shanghai, China

Funder

Innovation Program of Shanghai Municipal Education Commission

The Science and Technology Commission of Shanghai Municipality

National Natural Science Foundation of China

Incubating Program for Clinical Research and Innovation of Ren Ji Hospital, School of Medicine, Shanghai Jiao tong University

the Key Laboratory of Specialty Fiber Optics and Optical Access Networks

Publisher

Informa UK Limited

Subject

Computer Science Applications,Radiology Nuclear Medicine and imaging,Biomedical Engineering,Computational Mechanics

Link

https://www.tandfonline.com/doi/pdf/10.1080/21681163.2019.1608307

Reference28 articles.

1. An automatic method for identification of cystine crystals in urine sediment

2. Evaluation of a new automated microscopy urine sediment analyser – sediMAX conTRUST®

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