Automated breast cancer diagnosis using artificial neural network (ANN)

Early diagnosis and detection of breast cancer can be improved by deploying automated breast cancer applications. However, efficient algorithms have to be developed to detect texture features or morphological features or descriptor features that can possibility detect the presence of abnormalities in the breast. In this research work, image enhancement techniques, breast segmentation techniques, feature representation and classification methods have been explored and applied on mammograms and ultrasound images obtained from mini-MIAS and BCDR repositories. To predict the presence of lesions in images, Bayesian Neural Network (BNN) was adopted. This technique provides a sensitivity of 100% and is capable of extracting features from both mammograms and ultrasound images. To determine whether an image contains calcifications, which is a sign of the presence of cancer, support vector machine has been explored. The performance of the application is provided in terms of sensitivity, specificity, false positive and false negatives.