Adaptive Gradient Descent Backpropagation for Classification of Breast Tumors in Ultrasound Imaging

Abstract

This paper investigates and evaluates the performance of three gradient descent based backpropagation artificial neural network (ANN) algorithms in classifying the tumor as benign and malignant in ultrasound imaging. The ultrasound images were preprocessed by wavelet filters for reducing speckle noise. Fifty seven texture and shape attributes were extracted from filtered breast ultrasound images to classify breast tumors. Area under receiving operating curve (AUC), sensitivity, specificity, classification accuracy and CPU time were used as figure of merit for the classifier. Results show that adaptive gradient descent backpropagation based on variable learning rate outperformed other techniques giving highest classification accuracy of 84.6%.