title: Acute leukemia classification by ensemble particle swarm model selection.
abstract: 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
  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. We improved 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 using EPSMS provides 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.
authors.affiliation: National Institute of Astrophysics, Optics and Electronics, Department of Computational Sciences, Luis Enrique Erro # 1, Tonantzintla, Puebla 72840, Mexico. hugojair@inaoep.mx
authors.forename: Hugo Jair
authors.initials: HJ
authors.lastname: Escalante
authors.forename: Manuel
authors.initials: M
authors.lastname: Montes-y-Gómez
authors.forename: Jesús A
authors.initials: JA
authors.lastname: González
authors.forename: Pilar
authors.initials: P
authors.lastname: Gómez-Gil
authors.forename: Leopoldo
authors.initials: L
authors.lastname: Altamirano
authors.forename: Carlos A
authors.initials: CA
authors.lastname: Reyes
authors.forename: Carolina
authors.initials: C
authors.lastname: Reta
authors.forename: Alejandro
authors.initials: A
authors.lastname: Rosales
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001853:Bone Marrow / Q000473:pathology*; D001854:Bone Marrow Cells / Q000473:pathology*; D019337:Hematologic Neoplasms / Q000145:classification* / Q000175:diagnosis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007938:Leukemia / Q000145:classification* / Q000175:diagnosis; D010363:Pattern Recognition, Automated / Q000379:methods*