scholarly journals Segmentation and classification of brain tumor images using statistical feature extraction and deep neural networks

2021 ◽  
Vol 8 (85) ◽  
Author(s):  
Gaurish Joshi ◽  
Rajeev Kumar ◽  
Amit Kumar Singh Chauhan
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Deep Neural Networks ◽  
Statistical Feature

Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

2021 ◽  
Author(s):  
Luke Gundry ◽  
Gareth Kennedy ◽  
Alan Bond ◽  
Jie Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reaction Mechanisms ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Neural Network Classification ◽  
Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

scholarly journals Deep Neural Networks for Automatic Classification of Anesthetic-Induced Unconsciousness

2018 ◽  
pp. 216-225
Author(s):  
Konstantinos Patlatzoglou ◽  
Srivas Chennu ◽  
Mélanie Boly ◽  
Quentin Noirhomme ◽  
Vincent Bonhomme ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Classification

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.

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