A Study of Ultrasonic Liver Images Classification with Artificial Neural Networks Based on Fractal Geometry and Multiresolution Analysis

In this study, we evaluate the accuracy of classifiers for classification of ultrasonic liver tissues. Two different statistic classifiers ami three various artificial neural networks are included: Bayes classifier k-nearest neighbor classifier. Back-propagation neural networks, probabilistic neural network and modified probabilistic neural network. These Jive different classifiers were investigated to determine their ability to classify various categories of ultrasonic liver images. The investigation was performed on the basis of the same feature vector. For statistic classifiers the classification accuracy is at most 90.7% and with artificial neural networks the accuracy is at least 92%. The experimental results illustrated that artificial neural networks are an attractive alternative to conventional statistic techniques when dealing with classification task. Moreover, the feature vector based on fractal geometry and wavelet transform can provide good discriminant ability for ultrasonic liver images under study. Biomed Eng Appl Basis Comm. 2004(April); 16: 59-67.