CLASSIFICATION OF CIRRHOSIS FROM B-SCAN IMAGES USING PYRAMID NEURAL NETWORK

Abstract

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 a key technique used during 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 that of a system based on the conventional multilayer network architecture is compared. The result shows that compared to 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.