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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

24 Citations (Scopus)

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.

Original language	English
Pages (from-to)	742-752
Number of pages	11
Journal	International Journal of Control, Automation and Systems
Volume	13
Issue number	3
DOIs	https://doi.org/10.1007/s12555-012-0393-6
Publication status	Published - 1 Jun 2015
Externally published	Yes

Keywords

Feature extraction
fuzzy decision tree
fuzzy logic
leukemia diagnosis

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10.1007/s12555-012-0393-6

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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.",

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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

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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

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JF - International Journal of Control, Automation and Systems

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ER -

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

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Keywords

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