Classification method for degree of lung adenocarcinoma differentiation

Naoki Murakami, Toshiyuki Kanako, Toshiyuki Tanaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The number of fatalities from lung cancer accounts for 17% of that from all cancer, and is the highest ratio. Of them, the ratio of adenocarcinoma which has the highest ratio of lung cancer is increasing yearly. On the other hands, a classification of degree of differentiation is important to estimate prognosis, to determine the most suitable remedy and to investigate the relationship between smokers and patients of adenocarcinoma. Then we proposed new method for automatically classifying degree of adenocarcinoma differentiation. In this paper, we show the effectiveness of our method with results of classification.

Original languageEnglish
Title of host publicationSICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
PublisherSociety of Instrument and Control Engineers (SICE)
Pages1501-1504
Number of pages4
ISBN (Print)9784907764395
Publication statusPublished - 2011 Jan 1
Event50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
Duration: 2011 Sep 132011 Sep 18

Publication series

NameProceedings of the SICE Annual Conference

Other

Other50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011
CountryJapan
CityTokyo
Period11/9/1311/9/18

Keywords

  • case classification
  • image processing
  • lung cancer
  • neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Murakami, N., Kanako, T., & Tanaka, T. (2011). Classification method for degree of lung adenocarcinoma differentiation. In SICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts (pp. 1501-1504). [6060199] (Proceedings of the SICE Annual Conference). Society of Instrument and Control Engineers (SICE).