Performance Analysis of Morphological Operators Based Feature Extraction and SVD, Neural Networks as Post Classifier for the Classification of Epilepsy Risk Levels

2013 ◽  
pp. 1-11 ◽  
Author(s):  
R. Harikumar ◽  
T. Vijaykumar ◽  
C. Ganesh Babu ◽  
M. G. Sreejith
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Performance Analysis ◽  
Morphological Operators ◽  
Risk Levels

scholarly journals Pattern Recognition by Hierarchical Feature Extraction

2003 ◽  
Vol 15 (3) ◽  
pp. 278-285
Author(s):  
Daigo Misaki ◽  
◽  
Shigeru Aomura ◽  
Noriyuki Aoyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Input Data ◽  
Local Features ◽  
Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

Feature Extraction and Classification of Learners Using Neural Networks

2019 ◽  
Author(s):  
Tomohiro Hayashida ◽  
Toru Yamamoto ◽  
Shin Wakitani ◽  
Takuya Kinoshita ◽  
Ichiro Nishizaki ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction

Neural Networks for the Classification of Benign and Malignant Patters in Digital Mammograms

2006 ◽  
pp. 251-272 ◽  
Author(s):  
Brijesh Verma ◽  
Rinku Panchal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Digital Mammography ◽  
Extensive Review ◽  
Benchmark Database ◽  
Feature Extraction And Selection ◽  
Recent Developments ◽  
Artificial Neural Network Ann

This chapter presents neural network-based techniques for the classification of micro-calcification patterns in digital mammograms. Artificial neural network (ANN) applications in digital mammography are mainly focused on feature extraction, feature selection, and classification of micro-calcification patterns into ‘benign’ and ‘malignant’. An extensive review of neural network based techniques in digital mammography is presented. Recent developments such as auto-associators and evolutionary neural networks for feature extraction and selection are presented. Experimental results using ANN techniques on a benchmark database are described and analysed. Finally, a comparison of various neural network-based techniques is presented.

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