Evolutionary Neural Networks versus Adaptive Resonance Theory Net for Breast Cancer Diagnosis

2016 ◽  
Author(s):  
Tanistha Nayak ◽  
Tirtharaj Dash ◽  
D. Chandrasekhar Rao ◽  
Prabhat K. Sahu
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Adaptive Resonance Theory ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Evolutionary Neural Networks

scholarly journals Neural Networks Art: Solving Problems with Multiple Solutions and New Teaching Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 15-21
Author(s):  
Dmitrienko V. D ◽  
Yu. Zakovorotnyi A ◽  
Yu. Leonov S ◽  
Khavina I. P
Keyword(s):  
Neural Networks ◽  
Multiple Solutions ◽  
Input Data ◽  
Learning Algorithms ◽  
Adaptive Resonance Theory ◽  
Classification Methods ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Noisy Input ◽  
New Algorithms

A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input.

Breast Cancer Diagnosis Using Optimized Attribute Division in Modular Neural Networks

2013 ◽  
pp. 34-47
Author(s):  
Rahul Kala ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Optimal Distribution ◽  
Modular Neural Networks ◽  
Novel Concept ◽  
The Individual

The complexity of problems has led to a shift toward the use of modular neural networks in place of traditional neural networks. The number of inputs to neural networks must be kept within manageable limits to escape from the curse of dimensionality. Attribute division is a novel concept to reduce the problem dimensionality without losing information. In this paper, the authors use Genetic Algorithms to determine the optimal distribution of the parameters to the various modules of the modular neural network. The attribute set is divided into the various modules. Each module computes the output using its own list of attributes. The individual results are then integrated by an integrator. This framework is used for the diagnosis of breast cancer. Experimental results show that optimal distribution strategy exceeds the well-known methods for the diagnosis of the disease.

Using neural networks to select wavelet features for breast cancer diagnosis

1996 ◽  
Vol 15 (3) ◽  
pp. 95-102, 108 ◽  
Author(s):  
C.M. Kocur ◽  
S.K. Rogers ◽  
L.R. Myers ◽  
T. Burns ◽  
M. Kabrisky ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Wavelet Features
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