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A revised group method of data handling (GMDH)-type neural network algorithm for medical image recognition is proposed and is applied to 3-dimensional medical image analysis of the heart. The revised GMDH-type neural network algorithm has a feedback loop and can identify the characteristics of the medical images accurately using feedback loop calculations. In this algorithm, the polynomial type and...
The feedback group method of data handling (GMDH)-type neural network algorithm proposed in this paper is applied to 3-dimensional medical image recognition of the brain. The neural network architecture fitting the complexity of the medical images is automatically organized so as to minimize the prediction error criterion defined as Akaikepsilas information criterion (AIC) or prediction sum of squares...
We have developed a browsing tool for visualizing information about geographic surfaces using map-based augmented reality (AR). Map-based AR technology enables virtual objects to be overlaid on an actual map, creating a tangible user interface. In map-based AR applications, a virtual lens pointer is often used for object selection. However, this type of interaction is difficult when there are many...
In this study, three dimensional medical images of the lungs and brain are recognized and extracted by artificial neural networks. The neural networks used in this paper are the conventional sigmoid function neural network trained using back propagation (BP) algorithm and radial basis function (RBF) neural network. We compared the recognition results of these neural networks and determine which neural...
A radial basis function (RBF) group method of data handling (GMDH)-type neural network algorithm proposed in this paper is applied to the medical image recognition of abdominal X-ray CT images. The optimum neural network architecture for the medical image recognition is automatically organized using RBF GMDH-type neural network algorithm and the regions of abdominal organs such as the liver, stomach...
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