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Abstract #3072

Comparing supervised and unsupervised machine learning frameworks based upon quantitative-MRI features in differentiation between non-enhancing tumor and vasogenic edema of glioma patients and validation using histopathological ground-truth

Neha Vats1, Anirban Sengupta2, Dinil Sasi3, Rakesh Kumar Gupta4, R.P. Chauhan1, Virendra Kumar Yadav3, Sumeet Agarwal5, and Anup Singh3

1NIT Kurukshetra, Kurukshetra, India, 2Centre for Biomedical Engineering, IIT Delhi, New Delhi, India, 3IIT Delhi, New Delhi, India, 4Fortis Memorial Research Institute, New Delhi, India, 5Electrical Engineering, IIT Delhi, Hauz Khas, New Delhi, India

The aim of this study was to compare the efficacy of unsupervised machine learning technique in differentiating non-enhancing tumor(NET) from surrounding vasogenic edema (VE) in high-grade glioma patients using T1-perfusion MRI parameters. Two unsupervised machine learning techniques, k-means clustering and Gaussian mixture model (GMM) were optimized with respect to their hyper-parameters for differentiating NET from VE and the results were compared with previously published results obtained using a supervised classifier Support Vector Machine (SVM). The results showed that SVM classifier was slightly superior to GMM and K-means clustering in differentiating NET from VE.

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