The highest performance is seen in random forest model when data from all sequences are used in conjunction, achieving an overall classification accuracy of 83.7%. When using data from one single sequence, the overall accuracies achieved for T1 delayed, venous, arterial, and pre-contrast phase, T2, and T2 fat saturated were 79.1%, 70.5%, 56.2%, 61.0%, 60.0%, and 44.8%, respectively. This demonstrates promising results of utilizing intensity information from multiple MR sequences for accurate classification of renal masses.
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Uyen N. Hoang, Ashkan A. Malayeri, Nathan S. Lay, Ronald M. Summers, Jianhua Yao, "Texture analysis of common renal masses in multiple MR sequences for prediction of pathology," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343J (3 March 2017);