Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines

: In the case of Nijmegen dataset, the performance of the SVM was Az = 0.79 and 0.77 for the original and enhanced feature set, respectively, while for the MIAS dataset the corresponding characterization scores were Az = 0.81 and 0.80. Utilizing neural network classification methodology, the corresponding performance for the Nijmegen dataset was Az = 0.70 and 0.76 while for the MIAS dataset it was Az = 0.73 and 0.78. Although the obtained high classification performance can be successfully applied to microcalcification clusters characterization, further studies must be carried out for the clinical evaluation of the system using larger datasets. The use of additional features originating either from the image itself (such as cluster location and orientation) or from the patient data may further improve the diagnostic value of the system.