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 publicationProceedings of the SICE Annual Conference
Pages1501-1504
Number of pages4
Publication statusPublished - 2011
Event50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
Duration: 2011 Sep 132011 Sep 18

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

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

Cite this

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

Classification method for degree of lung adenocarcinoma differentiation. / Murakami, Naoki; Kanako, Toshiyuki; Tanaka, Toshiyuki.

Proceedings of the SICE Annual Conference. 2011. p. 1501-1504 6060199.

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

Murakami, N, Kanako, T & Tanaka, T 2011, Classification method for degree of lung adenocarcinoma differentiation. in Proceedings of the SICE Annual Conference., 6060199, pp. 1501-1504, 50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011, Tokyo, Japan, 11/9/13.
Murakami N, Kanako T, Tanaka T. Classification method for degree of lung adenocarcinoma differentiation. In Proceedings of the SICE Annual Conference. 2011. p. 1501-1504. 6060199
Murakami, Naoki ; Kanako, Toshiyuki ; Tanaka, Toshiyuki. / Classification method for degree of lung adenocarcinoma differentiation. Proceedings of the SICE Annual Conference. 2011. pp. 1501-1504
@inproceedings{9071eabb1ca443609460fd635528a68c,
title = "Classification method for degree of lung adenocarcinoma differentiation",
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.",
keywords = "case classification, image processing, lung cancer, neural network",
author = "Naoki Murakami and Toshiyuki Kanako and Toshiyuki Tanaka",
year = "2011",
language = "English",
isbn = "9784907764395",
pages = "1501--1504",
booktitle = "Proceedings of the SICE Annual Conference",

}

TY - GEN

T1 - Classification method for degree of lung adenocarcinoma differentiation

AU - Murakami, Naoki

AU - Kanako, Toshiyuki

AU - Tanaka, Toshiyuki

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - case classification

KW - image processing

KW - lung cancer

KW - neural network

UR - http://www.scopus.com/inward/record.url?scp=81255169839&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=81255169839&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:81255169839

SN - 9784907764395

SP - 1501

EP - 1504

BT - Proceedings of the SICE Annual Conference

ER -