Improving the performance of neural network in differentiation of breast tumors using wavelet transformation on dynamic MRI

ABSTRACT

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. The performance of the artificial neural network [ANN] was evaluated using a database with 105 patients' 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. 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]. An ANN supported by wavelet transform can be trained to differentiate malignant from benign breast tumors with a reasonable degree of accuracy