:: Volume 3, Issue 3 (12-2005) ::
Int J Radiat Res 2005, 3(3): 135-142 Back to browse issues page
Improving the performance of neural network in differentiation of breast tumors using wavelet transformation on dynamic MRI
P. Abdolmaleki , H. Abrishami-Moghddam, M. Gity, M. Mokhtari-Dizaji, A. Mostafa
, parviz@modares.ac.ir
Abstract:   (11279 Views)


 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' 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.

Keywords: Breast, neural network, wavelet transform, MR Imaging
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Type of Study: Original Research | Subject: Medical Physics

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Volume 3, Issue 3 (12-2005) Back to browse issues page