Investigating machine learning techniques for MRI-based classification of brain neoplasms

Evangelia I. Zacharaki, Vasileios G. Kanas, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


Purpose: 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.

Original languageEnglish (US)
Pages (from-to)821-828
Number of pages8
JournalInternational Journal of Computer Assisted Radiology and Surgery
Issue number6
StatePublished - Nov 2011
Externally publishedYes


  • Attribute selection
  • Brain tumor
  • Classification
  • MRI
  • Tumor grade

ASJC Scopus subject areas

  • Health Informatics
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Surgery
  • Biomedical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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