title: Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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 classification accuracy of 90% (92% specificity; 81% sensitivity).
  
  
  CONCLUSIONS
  Machine learning classifiers trained with texture analysis features are potentially valuable for detecting malignant tumors in T1-MRI images. Analysis of the learning curves of the classifiers showed that a training data size smaller than 100 T1-MRI images is sufficient to train a machine learning classifier that performs as well as expert radiologists.
authors.affiliation: Vision Laboratory (VisieLab), Department of Physics, University of Antwerp, Belgium.
authors.forename: Jaber
authors.initials: J
authors.lastname: Juntu
authors.forename: Jan
authors.initials: J
authors.lastname: Sijbers
authors.forename: Steve
authors.initials: S
authors.lastname: De Backer
authors.forename: Jeny
authors.initials: J
authors.lastname: Rajan
authors.forename: Dirk
authors.initials: D
authors.lastname: Van Dyck
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012983:Soft Tissue Neoplasms / Q000145:classification / Q000175:diagnosis*; D055815:Young Adult