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To study, from a machine learning perspective, the performance of several machine learning classifiers that use texture analysis features extracted from soft‐tissue tumors in nonenhanced T1‐MRI images to discriminate between malignant and benign tumors.

Texture analysis features were extracted from the tumor regions from T1‐MRI images of clinically proven cases of 49 malignant and 86 benign soft‐tissue tumors. Three conventional machine learning classifiers were trained and tested. The best classifier was compared to the radiologists by means of the McNemar's statistical test.

The SVM classifier performs better than the neural network and the C4.5 decision tree based on the analysis of their receiver operating curves (ROC) and cost curves. The classification accuracy of the SVM, which was 93% (91% specificity; 94% sensitivity), was better than the radiologist …