Int J Comput Assist Radiol Surg - Investigating machine learning techniques for MRI-based classification of brain neoplasms.

Tópicos

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Resumo

RPOSE: Diagnosis and characterization of brain neoplasms appears of utmost importance for therapeutic management. The emerging of imaging techniques, such as Magnetic Resonance (MR) imaging, gives insight into pathology, while the combination of several sequences from conventional and advanced protocols (such as perfusion imaging) increases the diagnostic information. To optimally combine the multiple sources and summarize the information into a distinctive set of variables however remains difficult. The purpose of this study is to investigate machine learning algorithms that automatically identify the relevant attributes and are optimal for brain tumor differentiation.METHODS: Different machine learning techniques are studied for brain tumor classification based on attributes extracted from conventional and perfusion MRI. The attributes, calculated from neoplastic, necrotic, and edematous regions of interest, include shape and intensity characteristics. Attributes subset selection is performed aiming to remove redundant attributes using two filtering methods and a wrapper approach, in combination with three different search algorithms (Best First, Greedy Stepwise and Scatter). The classification frameworks are implemented using the WEKA software.RESULTS: The highest average classification accuracy assessed by leave-one-out (LOO) cross-validation on 101 brain neoplasms was achieved using the wrapper evaluator in combination with the Best First search algorithm and the KNN classifier and reached 96.9% when discriminating metastases from gliomas and 94.5% when discriminating high-grade from low-grade neoplasms.CONCLUSIONS: A computer-assisted classification framework is developed and used for differential diagnosis of brain neoplasms based on MRI. The framework can achieve higher accuracy than most reported studies using MRI.

Resumo Limpo

rpose diagnosi character brain neoplasm appear utmost import therapeut manag emerg imag techniqu magnet reson mr imag give insight patholog combin sever sequenc convent advanc protocol perfus imag increas diagnost inform optim combin multipl sourc summar inform distinct set variabl howev remain difficult purpos studi investig machin learn algorithm automat identifi relev attribut optim brain tumor differentiationmethod differ machin learn techniqu studi brain tumor classif base attribut extract convent perfus mri attribut calcul neoplast necrot edemat region interest includ shape intens characterist attribut subset select perform aim remov redund attribut use two filter method wrapper approach combin three differ search algorithm best first greedi stepwis scatter classif framework implement use weka softwareresult highest averag classif accuraci assess leaveoneout loo crossvalid brain neoplasm achiev use wrapper evalu combin best first search algorithm knn classifi reach discrimin metastas glioma discrimin highgrad lowgrad neoplasmsconclus computerassist classif framework develop use differenti diagnosi brain neoplasm base mri framework can achiev higher accuraci report studi use mri

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