Microprocessor-based tissue classification using artificial neural net classifier
Nazeih M. Botros, H. Tee · 2002
Presents an algorithm and instrumentation for classifying liver tissue abnormalities. The instrumentation used is a 50-MHz microcomputer-based data acquisition and analysis system. The primary functions of the system are to digitize the backscattered ultrasound signal from a human liver tissue phantom, process these digitized data in the frequency domain, and apply pattern recognition algorithms to classify the abnormalities of simulated liver tissues. The pattern recognition algorithm is based on a three-layer back-propagation artificial neural network. The results show that the algorithm works satisfactorily for classifying simulated normal liver tissue and three types of simulated abnormalities.>