en Medical Physics Medical Physics تحقيق بديع Original Research <p align="center"><strong> ABSTRACT</strong></p><p> <strong>Background:</strong> A computer aided diagnosis system was established using the wavelet transform and neural network to differentiate malignant from benign in a </p><p>  group of patients with histo-pathologically proved breast lesions based on the data derived independ­ently from time-intensity profile. </p><p>  <strong>Materials and Methods:</strong> 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. </p><p>  <strong>Results:</strong> 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). </p><p><strong>Conclusion:</strong> An ANN supported by wavelet transform can be trained to differentiate malignant from benign breast tumors with a reason­able degree of accuracy. <i /></p> ,Breast,neural network,wavelet transform, MR Imaging 135 142 http://ijrr.com/browse.php?a_code=A-10-1-161&slc_lang=en&sid=1 P. Abdolmaleki parviz@modares.ac.ir 79003194753284600417 79003194753284600417 Yes H. Abrishami-Moghddam 79003194753284600418 79003194753284600418 No M. Gity 79003194753284600419 79003194753284600419 No M. Mokhtari-Dizaji 79003194753284600420 79003194753284600420 No A. Mostafa 79003194753284600421 79003194753284600421 No