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

Prayson RA, Agamanolis DP, Cohen ML, Estes ML (2000) Interobserver reproducibility among neuropathologists and surgical pathologists in fibrillary astrocytoma grading. J Neurol Sci 175(1): 33–39

Tate AR et al (2003) Automated classification of short echo time in in vivo 1H brain tumor spectra: a multicenter study. Magn Reson Med 49(1): 29–36

Majos C et al (2004) Brain tumor classification by proton MR spectroscopy: comparison of diagnostic accuracy at short and long TE. Am J Neuroradiol 25(10): 1696–1704

Huang Y, Lisboa PJG, El-Deredy W (2003) Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection. Stat Med 22(1): 147–164

Lu C, Devos A, Suykens JAK, Arus C, Huffel S Van (2007) Bagging linear sparse Bayesian learning models for variable selection in cancer diagnosis. IEEE Trans Inf Technol Med 11(3): 338–346

Li G, Yang J, Ye C, Geng D (2006) Degree prediction of malignancy in brain glioma using support vector machines. Comput Biol Med 36(3): 313–325

Devos A et al (2005) The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification. J Magn Reson 173(2): 218–228

Rajendran P, Madheswaran M (2009) An improved image mining technique for brain tumor classification using efficient classifier. Int J Comput Inf Secur 6(3)

Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62: 1609–1618

Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: An update. SIGKDD Explor 11(1)

Liu H, Yu L (2005) Towards integrating feature selection algorithm for classification and clustering. IEEE Trans Knowl Data Eng 17(4): 491–502

Hall MA (2000) Correlation-based feature selection for discrete and numeric class machine learning. In: 17th international conference on machine learning (ICML):359–366

Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151: 155–176

Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97: 273–324

Ghiselli EE (1964) Theory of psychological measurement. McGraw-Hill Book Co, New York

Xu L, Yan P, Chang T (1988) Best first strategy for feature selection. In: 9th international conference on pattern recognition, pp 706–708

Caruana R, Freitag D (1994) Greedy attribute selection. In: 11th international conference on machine learning, pp 28–36

Laguna M, Mart R (2003) Scatter search: methodology and implementations C. Kluwer, Dordrecht

Gandhi GM, Srivatsa SK (2010) Adaptive machine learning algorithm (AMLA) using J48 classifier for an NIDS environment. Adv Comput Sci Technol 3(3): 291–304

Cover TM, Hart PE (1967) Nearest neighbor pattern classification. Inst Electr Electron Eng Trans Inf Theory 13: 21–27

Demiroz G, Guvenir A (1997) Classification by voting feature intervals. In: 9th European conference on machine learning, pp 85–92

Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

John GH, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In: 11th conference on uncertainty in artificial intelligence, San Mateo, pp 338–345

Zacharaki EI, Wang S, Chawla S, Yoo DS, Wolf R, Melhem ER, Davatzikos C (2009) MRI-based classification of brain tumor type and grade using SVM-RFE. In: 6th IEEE International Symposis Biomedical Imaging (ISBI 2009), Boston, Massachusetts, USA

Huang Y-M, Du S-X (2005) weighted support vector machine for classification with uneven training class sizes. Int Conf Mach Learn Cybern 7: 4365–4369

Jolliffe IT (2002) Principal component analysis, series: springer series in statistics, 2nd edn. Springer, NY

Al-Okaili RN, Krejza J, Woo JH, Wolf RL, O’Rourke DM, Judy KD, Poptani H, Melhem ER (2007) Intraaxial brain masses: MR imaging–based diagnostic strategy—initial experience. Radiology 243: 539–550

Lev MH et al (2004) Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas. Am J Neuroradiol 25(2): 214–221