Title

Acute leukemia classification by ensemble particle swarm model selection

Author

Hugo Jair Escalante Balderas

Manuel Montes y Gómez

Jesús Antonio González Bernal

María del Pilar Gómez Gil

Leopoldo Altamirano Robles

CARLOS ALBERTO REYES GARCIA

CAROLINA RETA CASTRO

ALEJANDRO ROSALES PEREZ

Access level

Open Access

Summary or description

Objective: Acute leukemia is a malignant disease that affects a large proportion of the world population. Different types and subtypes of acute leukemia require different treatments. In order to assign the correct treatment, a physician must identify the leukemia type or subtype. Advanced and precise methods are available for identifying leukemia types, but they are very expensive and not available in most hospitals in developing countries. Thus, alternative methods have been proposed. An option explored in this paper is based on the morphological properties of bone marrow images, where features are extracted from medical images and standard machine learning techniques are used to build leukemia type classifiers. Methods and materials: This paper studies the use of ensemble particle swarm model selection (EPSMS), which is an automated tool for the selection of classification models, in the context of acute leukemia classification. EPSMS is the application of particle swarm optimization to the exploration of the search space of ensembles that can be formed by heterogeneous classification models in a machine learning toolbox. EPSMS does not require prior domain knowledge and it is able to select highly accurate classification models without user intervention. Furthermore, specific models can be used for different classification tasks. Results: We report experimental results for acute leukemia classification with real data and show that EPSMS outperformed the best results obtained using manually designed classifiers with the same data. The highest performance using EPSMS was of 97.68% for two-type classification problems and of 94.21% for more than two types problems. To the best of our knowledge, these are the best results reported for this data set. Compared with previous studies, these improvements were consistent among different type/subtype classification tasks, different features extracted from images, and different feature extraction regions. The performance improvements were statistically significant.Weimproved previous results by an average of 6% and there are improvements of more than 20% with some settings. In addition to the performance improvements, we demonstrated that no manual effort was required during acute leukemia type/subtype classification.

Conclusions: Morphological classification of acute leukemia usingEPSMSprovides an alternative to expensive diagnostic methods in developing countries. EPSMS is a highly effective method for the automated construction of ensemble classifiers for acute leukemia classification, which requires no significant user intervention. EPSMS could also be used to address other medical classification tasks.

Publisher

Elsevier B.V.

Publish date

2012

Publication type

Article

Publication version

Accepted Version

Format

application/pdf

Language

English

Audience

Students

Researchers

General public

Citation suggestion

Escalante-Balderas, H.J., et al., (2012). Acute leukemia classification by ensemble particle swarm model selection, Artificial Intelligence in Medicine, (55): 163–175

Source repository

Repositorio Institucional del INAOE

Downloads

100

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