It is challenging to differentiate between intra-cranial mass lesions (ICMLs) due to similar appearance using conventional MRIs. Amide-proton-transfer-weighted(APT-w) MRI provides differentiation among ICMLs with lower sensitivity ad specificity. The accuracy of classification between neo-plastic mass lesions and infective mass lesions as well as differentiation between low-grade-glioma and high-grade-glioma improves by implementing machine learning classifier based on features from APT-w CEST MRI. An optimized support-vector-machine with 10-fold cross-validation and optimal set of features extracted using Random forest based feature selection provided high accuracy of around 90%.
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