Automatic grading of prostate cancer (PCa) can play a major role in its early diagnosis, which has a significant impact on patient survival rates. The objective of this study was to develop and validate a framework for classification of PCa grades using texture features of T2-weighted MR images. Evaluation of classification result shows accuracy of 85.10 ± 2.43% using random forest feature selection and Gaussian support-vector machine classifier.
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