Genetic Selection of Fuzzy Model for Acute Leukemia Classification

  • Alejandro Rosales-Pérez
  • Carlos A. Reyes-García
  • Pilar Gómez-Gil
  • Jesus A. Gonzalez
  • Leopoldo Altamirano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)

Abstract

Leukemia is a disease characterized by an abnormal increase of white blood cells. This disease is divided into two types: lymphoblastic and myeloid, each of which is divided in subtypes. Differentiating the type and subtype of acute leukemia is important in order to determine the correct type of treatment to be assigned by the affected person. Diagnostic tests available today, such as those based on cell morphology, have a high error rate. Others, as those based on cytometry or microarray, are expensive. In order to avoid those drawbacks this paper proposes the automatic selection of a fuzzy model for accurate classification of types and subtypes of acute leukemia based on cell morphology. Our experimental results reach up to 93.52% in classification of acute leukemia types, 87.36% in lymphoblastic subtypes and 94.42% in myeloid subtypes. Our results show a significant improvement compared with classifiers which parameters were manually tuned using the same data set. Details of the proposed method, as well as experiments and results are shown.

Keywords

Leukemia classification selection of fuzzy model genetic algorithms 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alejandro Rosales-Pérez
    • 1
  • Carlos A. Reyes-García
    • 1
  • Pilar Gómez-Gil
    • 1
  • Jesus A. Gonzalez
    • 1
  • Leopoldo Altamirano
    • 1
  1. 1.Computer Science DepartmentNational Institute of Astrophysics, Optics and Electronics (INAOE)PueblaMexico

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