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

    研究成果: Conference contribution

    11 被引用数 (Scopus)


    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.

    ホスト出版物のタイトルISOCC 2016 - International SoC Design Conference
    ホスト出版物のサブタイトルSmart SoC for Intelligent Things
    出版社Institute of Electrical and Electronics Engineers Inc.
    出版ステータスPublished - 2016 12月 27
    イベント13th International SoC Design Conference, ISOCC 2016 - Jeju, Korea, Republic of
    継続期間: 2016 10月 232016 10月 26


    Other13th International SoC Design Conference, ISOCC 2016
    国/地域Korea, Republic of

    ASJC Scopus subject areas

    • ハードウェアとアーキテクチャ
    • 電子工学および電気工学
    • 器械工学


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