A voting-based encoding technique for the classification of gleason score for prostate cancers

Abstract

We present a novel approach for classifying the Gleason score for prostate tumours based on MRI data. Proposed approach uses three scores: 2, 3 and 4–5 (representing Gleason scores 4 and 5 as one single class). Patches are extracted from annotated MRI data for each of the class. Raw image patches have been used as features, instead of extracting manual hand-crafted features. Each patch is encoded using a dictionary and the encoded feature vector is then used for classification. A voting-based encoding approach is used to transform data from the image domain to more discriminative class-specific representations. Initial investigation demonstrated excellent results (Classification Accuracy equal to 85% and Area Under the ROC Curve (AUC) of 0.932) for 3-class Gleason score classification for prostate tumours.