Int J Comput Assist Radiol Surg - Building an ensemble system for diagnosing masses in mammograms.

Tópicos

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Resumo

RPOSE: Classification of a suspicious mass (region of interest, ROI) in a mammogram as malignant or benign may be achieved using mass shape features. An ensemble system was built for this purpose and tested.METHODS: Multiple contours were generated from a single ROI using various parameter settings of the image enhancement functions for the segmentation. For each segmented contour, the mass shape features were computed. For classification, the dataset was partitioned into four subsets based on the patient age (young/old) and the ROI size (large/small). We built an ensemble learning system consisting of four single classifiers, where each classifier is a specialist, trained specifically for one of the subsets. Those specialist classifiers are also an optimal classifier for the subset, selected from several candidate classifiers through preliminary experiment. In this scheme, the final diagnosis (malignant or benign) of an instance is the classification produced by the classifier trained for the subset to which the instance belongs.RESULTS: The Digital Database for Screening Mammography (DDSM) from the University of South Florida was used to test the ensemble system for classification of masses, which achieved a 72% overall accuracy. This ensemble of specialist classifiers achieved better performance than single classification (56%).CONCLUSION: An ensemble classifier for mammography-detected masses may provide superior performance to any single classifier in distinguishing benign from malignant cases.

Resumo Limpo

rpose classif suspici mass region interest roi mammogram malign benign may achiev use mass shape featur ensembl system built purpos testedmethod multipl contour generat singl roi use various paramet set imag enhanc function segment segment contour mass shape featur comput classif dataset partit four subset base patient age youngold roi size largesmal built ensembl learn system consist four singl classifi classifi specialist train specif one subset specialist classifi also optim classifi subset select sever candid classifi preliminari experi scheme final diagnosi malign benign instanc classif produc classifi train subset instanc belongsresult digit databas screen mammographi ddsm univers south florida use test ensembl system classif mass achiev overal accuraci ensembl specialist classifi achiev better perform singl classif conclus ensembl classifi mammographydetect mass may provid superior perform singl classifi distinguish benign malign case

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