Computer vision-based breast self-examination palpation pressure level classification using artificial neural networks and wavelet transforms

2012 ◽  
Author(s):  
Melvin K. Cabatuan ◽  
Elmer P. Dadios ◽  
Raouf N.G. Naguib
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Artificial Neural Networks ◽  
Wavelet Transforms ◽  
Pressure Level ◽  
Breast Self Examination ◽  
Artificial Neural ◽  
Self Examination

Honey characterization using computer vision system and artificial neural networks

2014 ◽  
Vol 159 ◽  
pp. 143-150 ◽  
Author(s):  
Sahameh Shafiee ◽  
Saeid Minaei ◽  
Nasrollah Moghaddam-Charkari ◽  
Mohsen Barzegar
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Artificial Neural Networks ◽  
Vision System ◽  
Computer Vision System ◽  
Artificial Neural

EPILEPTIC SPIKE DETECTION USING CONTINUOUS WAVELET TRANSFORMS AND ARTIFICIAL NEURAL NETWORKS

2010 ◽  
Vol 08 (01) ◽  
pp. 33-48 ◽  
Author(s):  
BERDAKH ABIBULLAEV ◽  
HEE DON SEO ◽  
MIN SOO KIM
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Wavelet Transforms ◽  
Continuous Wavelet ◽  
Continuous Wavelet Transforms ◽  
Feature Extraction Method ◽  
Artificial Neural ◽  
Epileptic Spike

We propose a new method for detection and classification of noisy recorded epileptic transients in Electroencephalograms (EEG) using the continuous wavelet transform (CWT) and artificial neural networks (ANN). The proposed method consists of a segmentation, feature extraction and classification stage. For the feature extraction stage, we use best basis mother wavelet functions and wavelet thresholding technique. For the classification stage, multilayer perceptron neural networks were implemented according to standard backpropagation learning formulations. We demonstrate the efficiency of our feature extraction method on data to improve the ANN detection performance. As a result, we achieved the accuracy in detection and classification of seizure EEG signals with 94.69%, which is relatively good comparing with the available algorithms at present time.

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