Ultrasonographic classification of cirrhosis based on pyramid neural network

This paper proposes a system applying a pyramid neural network for classifying the hepatic parenchymal diseases in ultrasonic B-scan texture. The conventional multilayer neural network emphasizing on the data carried by the last hidden layer has the drawback of not fully utilizing the information carried by the input data. A pyramid network can solve the problem successfully. To solve the common problem of neural network, which is time-consuming in computation, FDWT (fast discrete wavelet transform) is used as a key technique for preprocessing to cut down the size of patterns feed to the network. The B-scan patterns are wavelet transformed, and then the compressed data is fed into a pyramid neural network to diagnose the type of cirrhotic diseases. The performance of the proposed system and a system based on the conventional multilayer network architecture with is compared. The result shows that compared with the conventional 3-layer neural network, the performance of the proposed pyramid neural network is improved by effectively utilizing the lower layer of the neural network. The performance is examined in a series of computer simulations. It is shown that the proposed system, a pyramid neural network with 2 hidden layers, is suitable for diagnosis of cirrhosis