scholarly journals Medical Image Recognition by Revised GMDH-type Neural Network Algorithm with a Feedback Loop Identifying Sigmoid Function Neural Network

2006 ◽  
Vol 2006 (0) ◽  
pp. 143-148 ◽  
Author(s):  
Tadashi Kondo
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Medical Image ◽  
Feedback Loop ◽  
Sigmoid Function ◽  
Network Algorithm ◽  
Medical Image Recognition ◽  
Neural Network Algorithm

Revised GMDH-Type Neural Networks Using AIC or PSS Criterion and Their Application to Medical Image Recognition

2005 ◽  
Vol 9 (3) ◽  
pp. 257-267 ◽  
Author(s):  
Tadashi Kondo ◽  
◽  
Junji Ueno ◽  
Kazuya Kondo ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Recognition ◽  
Prediction Error ◽  
Medical Image ◽  
Group Method ◽  
Error Criterion ◽  
Network Algorithm ◽  
Medical Image Recognition ◽  
Neural Network Algorithm

This study deals with the revised Group Method of Data Handling (GMDH)-type neural network algorithm using prediction error criterion defined as Prediction Sum of Squares (PSS) or Akaike's Information Criterion (AIC). The revised GMDH-type neural network algorithm generates optimum multilayered neural network architectures fitting the complexity of nonlinear systems using heuristic self-organization. The revised GMDH-type neural networks self-select the number of layers, optimum neuronal architectures, and useful input variables to minimize prediction error criterion defined as PSS or AIC. This algorithm is applied to the identification problem of the nonlinear complex system and results are compared to those obtained by the revised GMDH algorithm and conventional multilayered neural networks. The revised GMDH-type neural network algorithm is also applied to medical image recognition and it is shown that this algorithm is useful for medical image recognition.

Multilayered GMDH-Type Neural Network with Radial Basis Functions and its Application to 3-Dimensional Medical Image Recognition of the Liver

2007 ◽  
Vol 11 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Tadashi Kondo ◽  
◽  
Junji Ueno ◽  
Abhijit S. Pandya ◽  
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Network Architecture ◽  
Prediction Error ◽  
Medical Image ◽  
Basis Functions ◽  
Rbf Network ◽  
3 Dimensional ◽  
Medical Image Recognition ◽  
Neural Network Algorithm

In this paper, a Group Method of Data Handling (GMDH)-type neural network algorithm with radial basis functions (RBF) is proposed. The proposed algorithm generates optimum RBF network architectures fitting the complexity of nonlinear systems using heuristic self-organization. The number of hidden layers, the number of neurons in hidden layers and relevant input variables are selected by minimizing prediction error defined as Akaike’s Information Criterion (AIC). Various nonlinear combinations of variables are initially generated in each layer and only relevant combinations are selected based on AIC. Hence, the optimum RBF network architecture fitting the complexity of the nonlinear system is obtained. We apply the GMDH-type neural network algorithm with RBF to 3-dimensional medical image recognition of the liver, showing that this algorithm is very easy and useful in 3-dimensional medical image recognition of the liver because the neural network architecture is automatically organized to minimize prediction error based on AIC.

Unmanned aerial vehicle reconnaissance image recognition based on convolutional neural network

2021 ◽  
pp. 1-10
Author(s):  
Lipeng Si ◽  
Baolong Liu ◽  
Yanfang Fu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Error Detection ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Small Target ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Real Time Tracking

The important strategic position of military UAVs and the wide application of civil UAVs in many fields, they all mark the arrival of the era of unmanned aerial vehicles. At present, in the field of image research, recognition and real-time tracking of specific objects in images has been a technology that many scholars continue to study in depth and need to be further tackled. Image recognition and real-time tracking technology has been widely used in UAV aerial photography. Through the analysis of convolution neural network algorithm and the comparison of image recognition technology, the convolution neural network algorithm is improved to improve the image recognition effect. In this paper, a target detection technique based on improved Faster R-CNN is proposed. The algorithm model is implemented and the classification accuracy is improved through Faster R-CNN network optimization. Aiming at the problem of small target error detection and scale difference in aerial data sets, this paper designs the network structure of RPN and the optimization scheme of related algorithms. The structure of Faster R-CNN is adjusted by improving the embedding of CNN and OHEM algorithm, the accuracy of small target and multitarget detection is improved as a whole. The experimental results show that: compared with LENET-5, the recognition accuracy of the proposed algorithm is significantly improved. And with the increase of the number of samples, the accuracy of this algorithm is 98.9%.

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