Deep learning application trial to lung cancer diagnosis for medical sensor systems

Ryota Shimizu; Shusuke Yanagawa; Yasutaka Monde; Hiroki Yamagishi; Mototsugu Hamada; Toru Shimizu; Tadahiro Kuroda

doi:10.1109/ISOCC.2016.7799852

Deep learning application trial to lung cancer diagnosis for medical sensor systems

Ryota Shimizu, Shusuke Yanagawa, Yasutaka Monde, Hiroki Yamagishi, Mototsugu Hamada, Toru Shimizu, Tadahiro Kuroda

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

10 Citations (Scopus)

Abstract

Personal and easy-To-use health checking system is an attractive application of sensor systems. Sensing data analysis for diagnosis is important as well as preparing small and mobile sensor nodes because sensing data include variations and noises reflecting individual difference of people and sensing conditions. Deep Neural Network, or Deep Learning, is a well-known method of machine learning and it is effective for feature extraction from pictures. Then, we thought Deep Learning also can extract features from sensing data. In this paper, we tried to build a diagnosis system of lung cancer based on Deep Learning. Input data of the system was generated from human urine by Gas Chromatography Mass Spectrometer (GC-MS) and our system achieved 90% accuracy in judging whether the patient had lung cancer or not. This system will be useful for pre-And personal diagnosis because collecting urine is very easy and not harmful to human body. We are targeting installation of this system not only to gas chromatography systems but also to some combination of multiple sensors for detecting gases of low concentration.

Original language	English
Title of host publication	ISOCC 2016 - International SoC Design Conference
Subtitle of host publication	Smart SoC for Intelligent Things
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	191-192
Number of pages	2
ISBN (Electronic)	9781467393089
DOIs	https://doi.org/10.1109/ISOCC.2016.7799852
Publication status	Published - 2016 Dec 27
Event	13th International SoC Design Conference, ISOCC 2016 - Jeju, Korea, Republic of Duration: 2016 Oct 23 → 2016 Oct 26

Other

Other	13th International SoC Design Conference, ISOCC 2016
Country/Territory	Korea, Republic of
City	Jeju
Period	16/10/23 → 16/10/26

Keywords

Deep learning
Deep neural network
Gas chromatography mass spectrometer(GC-MS)
Stacked autoencoder

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering
Instrumentation

UN SDGs

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

Access to Document

10.1109/ISOCC.2016.7799852

Cite this

Shimizu, R., Yanagawa, S., Monde, Y., Yamagishi, H., Hamada, M., Shimizu, T., & Kuroda, T. (2016). Deep learning application trial to lung cancer diagnosis for medical sensor systems. In ISOCC 2016 - International SoC Design Conference: Smart SoC for Intelligent Things (pp. 191-192). [7799852] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISOCC.2016.7799852

Deep learning application trial to lung cancer diagnosis for medical sensor systems. / Shimizu, Ryota; Yanagawa, Shusuke; Monde, Yasutaka et al.

ISOCC 2016 - International SoC Design Conference: Smart SoC for Intelligent Things. Institute of Electrical and Electronics Engineers Inc., 2016. p. 191-192 7799852.

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

Shimizu, R, Yanagawa, S, Monde, Y, Yamagishi, H, Hamada, M, Shimizu, T & Kuroda, T 2016, Deep learning application trial to lung cancer diagnosis for medical sensor systems. in ISOCC 2016 - International SoC Design Conference: Smart SoC for Intelligent Things., 7799852, Institute of Electrical and Electronics Engineers Inc., pp. 191-192, 13th International SoC Design Conference, ISOCC 2016, Jeju, Korea, Republic of, 16/10/23. https://doi.org/10.1109/ISOCC.2016.7799852

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AU - Shimizu, Toru

AU - Kuroda, Tadahiro

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AB - Personal and easy-To-use health checking system is an attractive application of sensor systems. Sensing data analysis for diagnosis is important as well as preparing small and mobile sensor nodes because sensing data include variations and noises reflecting individual difference of people and sensing conditions. Deep Neural Network, or Deep Learning, is a well-known method of machine learning and it is effective for feature extraction from pictures. Then, we thought Deep Learning also can extract features from sensing data. In this paper, we tried to build a diagnosis system of lung cancer based on Deep Learning. Input data of the system was generated from human urine by Gas Chromatography Mass Spectrometer (GC-MS) and our system achieved 90% accuracy in judging whether the patient had lung cancer or not. This system will be useful for pre-And personal diagnosis because collecting urine is very easy and not harmful to human body. We are targeting installation of this system not only to gas chromatography systems but also to some combination of multiple sensors for detecting gases of low concentration.

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Deep learning application trial to lung cancer diagnosis for medical sensor systems

Abstract

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Keywords

ASJC Scopus subject areas

UN SDGs

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