Medical image analysis of abdominal X-ray CT images by deep multi-layered GMDH-type neural network

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 m...

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Bibliographic Details
Published inArtificial life and robotics Vol. 23; no. 2; pp. 271 - 278
Main Authors Takao, Shoichiro, Kondo, Sayaka, Ueno, Junji, Kondo, Tadashi
Format Journal Article
LanguageEnglish
Published Tokyo Springer Japan 01.06.2018
Springer Nature B.V
Subjects
Abdomen
Artificial Intelligence
Artificial neural networks
Computation by Abstract Devices
Computed tomography
Computer architecture
Computer Science
Control
Criteria
Group method of data handling
Image analysis
Mechatronics
Medical imaging
Multilayers
Neural networks
Object recognition
Original Article
Robotics
Medical image recognition
Deep neural networks
GMDH
Machine learning
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Summary: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.
ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-017-0420-z