Medical Image Diagnosis of Liver Cancer Using a Neural Network and Artificial Intelligence
Tadashi Kondo, Junji Ueno, and Shoichiro Takao
Graduate School of Health Sciences, The University of Tokushima, 3-18-15 Kuramoto-cho, Tokushima 770-8509, Japan
A revised Group Method of Data Handling (GMDH)-type neural network algorithm using artificial intelligence technology for medical image diagnosis is proposed and is applied to medical image diagnosis of liver cancer. In this algorithm, the knowledge base for medical image diagnosis is used in organizing the neural network architecture for medical image diagnosis. Furthermore, the revisedGMDH-type neural network algorithm has a feedback loop and can identify the characteristics of the medical images accurately using feedback loop calculations. The neural network architecture that optimally fit the complexity of the medical images, is automatically organized so as to minimize the prediction error criterion defined as Prediction Sum of Squares (PSS). It is shown that the revised GMDH-type neural network is accurate and a useful method for the medical image diagnosis of the liver cancer.
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