Deep multi-layered GMDH-type neural network using revised heuristic self-organization and its application to medical image diagnosis of liver cancer

2017 ◽  
Vol 23 (1) ◽  
pp. 48-59 ◽  
Author(s):  
Shoichiro Takao ◽  
Sayaka Kondo ◽  
Junji Ueno ◽  
Tadashi Kondo
Keyword(s):  
Neural Network ◽  
Liver Cancer ◽  
Medical Image ◽  
Self Organization ◽  
Medical Image Diagnosis ◽  
Image Diagnosis

Medical Image Diagnosis of Liver Cancer Using a Neural Network and Artificial Intelligence

2011 ◽  
Vol 15 (6) ◽  
pp. 714-722 ◽  
Author(s):  
Tadashi Kondo ◽  
◽  
Junji Ueno ◽  
Shoichiro Takao
Keyword(s):  
Neural Network ◽  
Liver Cancer ◽  
Network Architecture ◽  
Medical Image ◽  
Feedback Loop ◽  
Neural Network Architecture ◽  
The Neural Network ◽  
Medical Image Diagnosis ◽  
Image Diagnosis ◽  
Neural Network Algorithm

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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