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. 2011 Nov;6(6):821-8.
doi: 10.1007/s11548-011-0559-3. Epub 2011 Apr 23.

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

Evangelia I Zacharaki  1 , Vasileios G Kanas, Christos Davatzikos
Affiliations

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

Evangelia I Zacharaki et al. Int J Comput Assist Radiol Surg. 2011 Nov.

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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Figures

Fig. 1
Fig. 1
Examples of preprocessed axial T1 contrast-enhanced images (1st row) and FLAIR images (2nd row) with brain neoplasms. From left to right: meningioma, glioma grade II, III, IV, and metastasis
Fig. 2
Fig. 2
Average classification accuracy of primary neoplasms (gliomas) versus metastases
Fig. 3
Fig. 3
Average classification accuracy of low versus high-grade gliomas
Fig. 4
Fig. 4
Attribute selection via PCA. The accuracy of a KNN (k = 3) classifier is shown versus the number of retained components for the classification of low versus high-grade gliomas and gliomas versus metastasis
Fig. 5
Fig. 5
Accuracy for the multiclass problem
See this image and copyright information in PMC

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