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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

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.

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Authors and Affiliations

  1. Department of Computer Engineering & Informatics, University of Patras, Patras, Greece

    Evangelia I. Zacharaki

  2. Department of Electrical and Computer Engineering, University of Patras, Patras, Greece

    Vasileios G. Kanas

  3. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA

    Christos Davatzikos

Authors
  1. Evangelia I. Zacharaki
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  2. Vasileios G. Kanas
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  3. Christos Davatzikos
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Corresponding author

Correspondence to Evangelia I. Zacharaki.

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Zacharaki, E.I., Kanas, V.G. & Davatzikos, C. Investigating machine learning techniques for MRI-based classification of brain neoplasms. Int J CARS 6, 821–828 (2011). https://doi.org/10.1007/s11548-011-0559-3

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  • DOI: https://doi.org/10.1007/s11548-011-0559-3

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