Establish a Medical Image Analysis and Identification Platform

Hsiao Hsuan Chen; Yin Chen Chen; Hsiu An Lee; Chien Yeh Hsu

doi:10.1007/978-981-15-3250-4_236

Establish a Medical Image Analysis and Identification Platform

Hsiao Hsuan Chen
, Yin Chen Chen
, Hsiu An Lee
, Chien Yeh Hsu

Master Program in Global Health and Health Security

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The proportion of China’s 65-year-old population has increased year by year. As we enter old age, people’s average life expectancy increases year by year. The risk of disease in the elderly population increases. According to the statistics of the Ministry of Health and Welfare of Taiwan, most people over the age of 65 suffer from cancer and heart disease. Pneumonia, cerebrovascular disease, and diabetes. With medical advancement, more and more disease characteristics can be found in medical imaging. With the development of technology, the higher the resolution of the image, the more information that can be contained, and the doctor will spend more time marking and diagnosing. There is currently no open imaging training and recognition platform for users to use, so this study uses deep learning CNN technology to construct an image analysis and recognition platform with analysis and recognition interface. In the future, retraining and batch identification capabilities can be added to complete the integrity of the platform.

Original language	English
Title of host publication	Frontier Computing - Theory, Technologies and Applications, FC 2019
Editors	Jason C. Hung, Jia-Wei Chang, Neil Y. Yen
Publisher	Springer
Pages	1766-1775
Number of pages	10
ISBN (Print)	9789811532498
DOIs	https://doi.org/10.1007/978-981-15-3250-4_236
Publication status	Published - 2020
Event	9th International Conference on Frontier Computing, FC 2019 - Kyushu, Japan Duration: Jul 9 2019 → Jul 12 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	551 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	9th International Conference on Frontier Computing, FC 2019
Country/Territory	Japan
City	Kyushu
Period	7/9/19 → 7/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

CNN
Convolution neural network
Medical image
Platform

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-15-3250-4_236

Cite this

Establish a Medical Image Analysis and Identification Platform. / Chen, Hsiao Hsuan; Chen, Yin Chen; Lee, Hsiu An et al.
Frontier Computing - Theory, Technologies and Applications, FC 2019. ed. / Jason C. Hung; Jia-Wei Chang; Neil Y. Yen. Springer, 2020. p. 1766-1775 (Lecture Notes in Electrical Engineering; Vol. 551 LNEE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, HH, Chen, YC, Lee, HA & Hsu, CY 2020, Establish a Medical Image Analysis and Identification Platform. in JC Hung, J-W Chang & NY Yen (eds), Frontier Computing - Theory, Technologies and Applications, FC 2019. Lecture Notes in Electrical Engineering, vol. 551 LNEE, Springer, pp. 1766-1775, 9th International Conference on Frontier Computing, FC 2019, Kyushu, Japan, 7/9/19. https://doi.org/10.1007/978-981-15-3250-4_236

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title = "Establish a Medical Image Analysis and Identification Platform",

abstract = "The proportion of China{\textquoteright}s 65-year-old population has increased year by year. As we enter old age, people{\textquoteright}s average life expectancy increases year by year. The risk of disease in the elderly population increases. According to the statistics of the Ministry of Health and Welfare of Taiwan, most people over the age of 65 suffer from cancer and heart disease. Pneumonia, cerebrovascular disease, and diabetes. With medical advancement, more and more disease characteristics can be found in medical imaging. With the development of technology, the higher the resolution of the image, the more information that can be contained, and the doctor will spend more time marking and diagnosing. There is currently no open imaging training and recognition platform for users to use, so this study uses deep learning CNN technology to construct an image analysis and recognition platform with analysis and recognition interface. In the future, retraining and batch identification capabilities can be added to complete the integrity of the platform.",

keywords = "CNN, Convolution neural network, Medical image, Platform",

author = "Chen, \{Hsiao Hsuan\} and Chen, \{Yin Chen\} and Lee, \{Hsiu An\} and Hsu, \{Chien Yeh\}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 9th International Conference on Frontier Computing, FC 2019 ; Conference date: 09-07-2019 Through 12-07-2019",

year = "2020",

doi = "10.1007/978-981-15-3250-4\_236",

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series = "Lecture Notes in Electrical Engineering",

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T1 - Establish a Medical Image Analysis and Identification Platform

AU - Chen, Hsiao Hsuan

AU - Chen, Yin Chen

AU - Lee, Hsiu An

AU - Hsu, Chien Yeh

PY - 2020

Y1 - 2020

N2 - The proportion of China’s 65-year-old population has increased year by year. As we enter old age, people’s average life expectancy increases year by year. The risk of disease in the elderly population increases. According to the statistics of the Ministry of Health and Welfare of Taiwan, most people over the age of 65 suffer from cancer and heart disease. Pneumonia, cerebrovascular disease, and diabetes. With medical advancement, more and more disease characteristics can be found in medical imaging. With the development of technology, the higher the resolution of the image, the more information that can be contained, and the doctor will spend more time marking and diagnosing. There is currently no open imaging training and recognition platform for users to use, so this study uses deep learning CNN technology to construct an image analysis and recognition platform with analysis and recognition interface. In the future, retraining and batch identification capabilities can be added to complete the integrity of the platform.

AB - The proportion of China’s 65-year-old population has increased year by year. As we enter old age, people’s average life expectancy increases year by year. The risk of disease in the elderly population increases. According to the statistics of the Ministry of Health and Welfare of Taiwan, most people over the age of 65 suffer from cancer and heart disease. Pneumonia, cerebrovascular disease, and diabetes. With medical advancement, more and more disease characteristics can be found in medical imaging. With the development of technology, the higher the resolution of the image, the more information that can be contained, and the doctor will spend more time marking and diagnosing. There is currently no open imaging training and recognition platform for users to use, so this study uses deep learning CNN technology to construct an image analysis and recognition platform with analysis and recognition interface. In the future, retraining and batch identification capabilities can be added to complete the integrity of the platform.

KW - CNN

KW - Convolution neural network

KW - Medical image

KW - Platform

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U2 - 10.1007/978-981-15-3250-4_236

DO - 10.1007/978-981-15-3250-4_236

M3 - Conference contribution

AN - SCOPUS:85082319165

SN - 9789811532498

T3 - Lecture Notes in Electrical Engineering

SP - 1766

EP - 1775

BT - Frontier Computing - Theory, Technologies and Applications, FC 2019

A2 - Hung, Jason C.

A2 - Chang, Jia-Wei

A2 - Yen, Neil Y.

PB - Springer

T2 - 9th International Conference on Frontier Computing, FC 2019

Y2 - 9 July 2019 through 12 July 2019

ER -

Establish a Medical Image Analysis and Identification Platform

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this