Med Biol Eng Comput - Feature selection and classification of leukocytes using random forest.

Tópicos

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Resumo

In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space. Therefore, this paper introduces a novel Gini importance-based binary random forest feature selection method. Moreover, the random forest classifier is used to classify the extracted objects into artifacts, mononuclear cells, and polymorphonuclear cells. The experimental results establish that the proposed method effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods.

Resumo Limpo

automat segment leukocyt complex morpholog background tissu section imag vast number artifactsnois also extract caus larg amount multivari data generat multivari data degrad perform classifi discrimin leukocyt artifactsnois howev select promin featur play import role reduc comput complex increas perform classifi compar highdimension featur space therefor paper introduc novel gini importancebas binari random forest featur select method moreov random forest classifi use classifi extract object artifact mononuclear cell polymorphonuclear cell experiment result establish propos method effect elimin irrelev featur maintain high classif accuraci compar featur reduct method

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