Fuzzy feature representation for white blood cell differential counting in acute leukemia diagnosis

Chastine Fatichah; Martin L. Tangel; Fei Yan; Janet P. Betancourt; M. Rahmat Widyanto; Fangyan Dong; Kaoru Hirota

doi:10.1007/s12555-012-0393-6

Fuzzy feature representation for white blood cell differential counting in acute leukemia diagnosis

Chastine Fatichah^*, Martin L. Tangel, Fei Yan, Janet P. Betancourt, M. Rahmat Widyanto, Fangyan Dong, Kaoru Hirota

^*此作品的通讯作者

科研成果: 期刊稿件 › 文章 › 同行评审

24 引用（Scopus）

摘要

A fuzzy feature representation for white blood cell differential counting is proposed to diagnose types of acute leukemia. The accuracy of diagnosis is higher than that by numerical features by dealing with uncertainty of white blood cell features and inflexibility of diagnosing. Experiments on acute leukemia diagnosis use 120 acute leukemia images and fuzzy decision tree method with the accuracy rate of diagnosis is 84% using fuzzy features and 76.6% using numerical features. Given the importance of accurate diagnosis of acute leukemia in patients, this proposal is essential and planned to be introduced in an Indonesian hospital.

源语言	英语
页（从-至）	742-752
页数	11
期刊	International Journal of Control, Automation and Systems
卷	13
期	3
DOI	https://doi.org/10.1007/s12555-012-0393-6
出版状态	已出版 - 1 6月 2015
已对外发布	是

访问文件

10.1007/s12555-012-0393-6

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{55b254f3663c49a5b046d948be8e2dcf,

title = "Fuzzy feature representation for white blood cell differential counting in acute leukemia diagnosis",

abstract = "A fuzzy feature representation for white blood cell differential counting is proposed to diagnose types of acute leukemia. The accuracy of diagnosis is higher than that by numerical features by dealing with uncertainty of white blood cell features and inflexibility of diagnosing. Experiments on acute leukemia diagnosis use 120 acute leukemia images and fuzzy decision tree method with the accuracy rate of diagnosis is 84% using fuzzy features and 76.6% using numerical features. Given the importance of accurate diagnosis of acute leukemia in patients, this proposal is essential and planned to be introduced in an Indonesian hospital.",

keywords = "Feature extraction, fuzzy decision tree, fuzzy logic, leukemia diagnosis",

author = "Chastine Fatichah and Tangel, {Martin L.} and Fei Yan and Betancourt, {Janet P.} and Widyanto, {M. Rahmat} and Fangyan Dong and Kaoru Hirota",

note = "Publisher Copyright: {\textcopyright} 2015, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg.",

year = "2015",

month = jun,

day = "1",

doi = "10.1007/s12555-012-0393-6",

language = "English",

volume = "13",

pages = "742--752",

journal = "International Journal of Control, Automation and Systems",

issn = "1598-6446",

publisher = "Institute of Control, Robotics and Systems",

number = "3",

}

TY - JOUR

T1 - Fuzzy feature representation for white blood cell differential counting in acute leukemia diagnosis

AU - Fatichah, Chastine

AU - Tangel, Martin L.

AU - Yan, Fei

AU - Betancourt, Janet P.

AU - Widyanto, M. Rahmat

AU - Dong, Fangyan

AU - Hirota, Kaoru

PY - 2015/6/1

Y1 - 2015/6/1

N2 - A fuzzy feature representation for white blood cell differential counting is proposed to diagnose types of acute leukemia. The accuracy of diagnosis is higher than that by numerical features by dealing with uncertainty of white blood cell features and inflexibility of diagnosing. Experiments on acute leukemia diagnosis use 120 acute leukemia images and fuzzy decision tree method with the accuracy rate of diagnosis is 84% using fuzzy features and 76.6% using numerical features. Given the importance of accurate diagnosis of acute leukemia in patients, this proposal is essential and planned to be introduced in an Indonesian hospital.

AB - A fuzzy feature representation for white blood cell differential counting is proposed to diagnose types of acute leukemia. The accuracy of diagnosis is higher than that by numerical features by dealing with uncertainty of white blood cell features and inflexibility of diagnosing. Experiments on acute leukemia diagnosis use 120 acute leukemia images and fuzzy decision tree method with the accuracy rate of diagnosis is 84% using fuzzy features and 76.6% using numerical features. Given the importance of accurate diagnosis of acute leukemia in patients, this proposal is essential and planned to be introduced in an Indonesian hospital.

KW - Feature extraction

KW - fuzzy decision tree

KW - fuzzy logic

KW - leukemia diagnosis

UR - http://www.scopus.com/inward/record.url?scp=84930090083&partnerID=8YFLogxK

U2 - 10.1007/s12555-012-0393-6

DO - 10.1007/s12555-012-0393-6

M3 - Article

AN - SCOPUS:84930090083

SN - 1598-6446

VL - 13

SP - 742

EP - 752

JO - International Journal of Control, Automation and Systems

JF - International Journal of Control, Automation and Systems

IS - 3

ER -

Fuzzy feature representation for white blood cell differential counting in acute leukemia diagnosis

摘要

访问文件

其它文件与链接

指纹

引用此