México | info:eu-repo/semantics/article
dc.creatorHugo Jair Escalante Balderas
dc.creatorManuel Montes y Gómez
dc.creatorJesús Antonio González Bernal
dc.creatorMaría del Pilar Gómez Gil
dc.creatorLeopoldo Altamirano Robles
dc.creatorCARLOS ALBERTO REYES GARCIA
dc.creatorCAROLINA RETA CASTRO
dc.creatorALEJANDRO ROSALES PEREZ
dc.date2012
dc.date.accessioned2023-07-25T16:24:28Z
dc.date.available2023-07-25T16:24:28Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1862
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7807053
dc.descriptionObjective: 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.
dc.descriptionConclusions: 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.
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier B.V.
dc.relationcitation:Escalante-Balderas, H.J., et al., (2012). Acute leukemia classification by ensemble particle swarm model selection, Artificial Intelligence in Medicine, (55): 163–175
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/Ensemble learning/Ensemble learning
dc.subjectinfo:eu-repo/classification/Swarm optimization/Swarm optimization
dc.subjectinfo:eu-repo/classification/Full model selection/Full model selection
dc.subjectinfo:eu-repo/classification/Morphological classification/Morphological classification
dc.subjectinfo:eu-repo/classification/Analysis of bone marrow cell images/Analysis of bone marrow cell images
dc.subjectinfo:eu-repo/classification/Acute leukemia classification/Acute leukemia classification
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/12
dc.subjectinfo:eu-repo/classification/cti/1203
dc.subjectinfo:eu-repo/classification/cti/1203
dc.titleAcute leukemia classification by ensemble particle swarm model selection
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


Este ítem pertenece a la siguiente institución