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 proceedingConference contribution

    8 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 languageEnglish
    Title of host publicationISOCC 2016 - International SoC Design Conference
    Subtitle of host publicationSmart SoC for Intelligent Things
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages191-192
    Number of pages2
    ISBN (Electronic)9781467393089
    DOIs
    Publication statusPublished - 2016 Dec 27
    Event13th International SoC Design Conference, ISOCC 2016 - Jeju, Korea, Republic of
    Duration: 2016 Oct 232016 Oct 26

    Other

    Other13th International SoC Design Conference, ISOCC 2016
    CountryKorea, Republic of
    CityJeju
    Period16/10/2316/10/26

    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

    Fingerprint Dive into the research topics of 'Deep learning application trial to lung cancer diagnosis for medical sensor systems'. Together they form a unique fingerprint.

  • 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