improving the performance of neural network in differentiation of breast tumors using wavelet transformation on dynamic mri

abstract background: a computer aided diagnosis system was established using the wavelet transform and neural network to differentiate malignant from benign in a   group of patients with histo-pathologically proved breast lesions based on the data derived independ­ently from time-intensity profile.   materials and methods: the per­formance of the artificial neural network (ann) was evaluated using a database with 105 patients&apos; records each of which consisted of 8 quantitative parameters mostly derived from time-intensity profile using wavelet transform. these findings were encoded as features for a three-layered neural network to predict the outcome of biopsy. the network was trained and tested using the jack­knife method and its performance was then compared to that of the radiologists in terms of sensitiv­ity, specificity and accuracy using receiver operating characteristic curve (roc) analysis.   results: the network was able to classify correctly the 84 original cases and yielded a comparable diagnostic accuracy (80%), compared to that of the radiologist (85%) by per­forming a constructive association between extracted quantitative data and correspond­ing pathological results (r=0.63, p<0.001). conclusion: an ann supported by wavelet transform can be trained to differentiate malignant from benign breast tumors with a reason­able degree of accuracy.