DocumentCode
2033079
Title
Classification method for degree of lung adenocarcinoma differentiation
Author
Murakami, Naoki ; Kanako, Toshiyuki ; Tanaka, Toshiyuki
Author_Institution
Sch. of Fundamental Sci. & Technol., Keio Univ., Yokohama, Japan
fYear
2011
fDate
13-18 Sept. 2011
Firstpage
1501
Lastpage
1504
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
cancer; differentiation; image classification; lung; medical image processing; neural nets; lung adenocarcinoma differentiation degree classification; lung cancer; neural network topology; Accuracy; Biological neural networks; Cancer; Cavity resonators; Correlation; Feature extraction; Lungs; case classification; image processing; lung cancer; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
SICE Annual Conference (SICE), 2011 Proceedings of
Conference_Location
Tokyo
ISSN
pending
Print_ISBN
978-1-4577-0714-8
Type
conf
Filename
6060199
Link To Document