Non-linear Least Squares Features Transformation for Improving the Performance of Probabilistic Neural Networks in Classifying Human Brain Tumors on MRI

The aim of the present study was to design, implement, and evaluate a software system for discriminating between metastases, meningiomas, and gliomas on MRI. The proposed classifier is a modified probabilistic neural network (PNN), incorporating a second degree least squares features transformation (LSFT) into the PNN classifier. Thirty-six textural features were extracted from each one of 75 T1-weighted post-contrast MR images (24 metastases, 21 meningiomas, and 30 gliomas). Classification performance was evaluated employing the leave-one-out method and for all possible textural feature combinations. LSFT enhanced the performance of the PNN, achieving 93.33% in discriminating between the three major types of human brain tumors, against 89.33% scored by the PNN alone. Best feature combination for achieving highest discrimination power included the mean value and entropy, which reflect specific properties of texture, i.e. signal strength and inhomogeneity. LSFT improved PNN performance, increased class separability, and resulted in dimensionality reduction.