Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network
| Autor: | Sayaka Kondo, Junji Ueno, Shoichiro Takao, Tadashi Kondo |
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| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
Group method of data handling Computer science business.industry Mean squared prediction error 0206 medical engineering Explained sum of squares Pattern recognition 02 engineering and technology 020601 biomedical engineering General Biochemistry Genetics and Molecular Biology Image (mathematics) 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Neural network architecture Imaging diagnosis Artificial intelligence Akaike information criterion business 030217 neurology & neurosurgery |
| Zdroj: | Artificial Life and Robotics. 23:271-278 |
| ISSN: | 1614-7456 1433-5298 |
| Popis: | In this study, a deep multi-layered group method of data handling (GMDH)-type neural network is applied to the medical image analysis of the abdominal X-ray computed tomography (CT) images. The deep neural network architecture which has many hidden layers are automatically organized using the deep multi-layered GMDH-type neural network algorithm so as to minimize the prediction error criterion defined as Akaike’s information criterion (AIC) or prediction sum of squares (PSS). The characteristics of the medical images are very complex and therefore the deep neural network architecture is very useful for the medical image diagnosis and medical image recognition. In this study, it is shown that this deep multi-layered GMDH-type neural network is useful for the medical image analysis of abdominal X-ray CT images. |
| Databáze: | OpenAIRE |
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