An Attention-based CNN-BiLSTM Hybrid Neural Network Enhanced with Features of Discrete Wavelet Transformation for Fetal Acidosis Classification

2021 ◽  
pp. 115714
Author(s):  
Mujun Liu ◽  
Yaosheng Lu ◽  
Shun Long ◽  
Jieyun Bai ◽  
Wanmin Lian
Keyword(s):  
Neural Network ◽  
Wavelet Transformation ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transformation ◽  
Hybrid Neural Network ◽  
Fetal Acidosis

scholarly journals Study on the early detection of gastric cancer based on discrete wavelet transformation feature extraction of FT-IR spectra combined with probability neural network

2011 ◽  
Vol 26 (3) ◽  
pp. 155-165 ◽  
Author(s):  
Tao Hu ◽  
Yu-Hui Lu ◽  
Cun-Gui Cheng ◽  
Xiao-Chen Sun
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Feature Extraction ◽  
Ir Spectra ◽  
Wavelet Transformation ◽  
Gastric Cancer Tissue ◽  
Discrete Wavelet ◽  
Cancer Tissue ◽  
Discrete Wavelet Transformation ◽  
Ft Ir

This paper introduces a new method for the early detection of gastric cancer using a combination of feature extraction based on discrete wavelet transformation (DWT) for horizontal attenuated total reflectance–Fourier transform infrared spectroscopy (HATR–FT-IR) and classification using probability neural network (PNN). 344 FT-IR spectra were collected from 172 pairs of fresh normal and abnormal stomach tissue᾽s samples. After preprocessing, 5 features were extracted with DWT analysis. Based on the PNN classification, all FT-IR spectra were classified into three categories. The accuracy of identifying normal gastric tissue, early gastric cancer tissue and gastric cancer tissue samples were 100.00, 97.56 and 100.00%, respectively. This result indicated that FT-IR with DWT and PNN could effectively and easily diagnose gastric cancer in its early stages.

scholarly journals Recognition of MIR Data of Semen Armeniacae Amarum and Semen Persicae Using Discrete Wavelet Transformation and BP-Artificial Neural Network

2012 ◽  
Vol 27 ◽  
pp. 253-264 ◽  
Author(s):  
Cun-Gui Cheng ◽  
Peng Yu ◽  
Chang-Shun Wu ◽  
Jia-Ni Shou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transformation ◽  
Feature Vector ◽  
Recognition Rate ◽  
Back Propagation ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transformation ◽  
Artificial Neural ◽  
The Difference

Horizontal attenuation total reflection-Fourier transformation infrared spectroscopy (HATR-FT-IR) is used to measure the Mid-IR (MIR) of semen armeniacae amarum and its confusable varieties semen persicae. In order to extrude the difference between semen armeniacae amarum and semen persicae, discrete wavelet transformation (DWT) is used to decompose the MIR of semen armeniacae amarum and semen persicae. Two main scales are selected as the feature extracting space in the DWT domain. According to the distribution of semen armeniacae amarum and semen persicae’s MIR, five feature regions are determined at every spectra band by selecting two scales in the DWT domain. Thus, ten feature parameters form the feature vector. The feature vector is input to the back-propagation artificial neural network (BP-ANN) to train so as to accurately classify the semen armeniacae amarum and semen persicae. 100 couples of MIR are used to train and test the proposed method, where 50 couples of data are used to train samples and other 50 couples of data are used to test samples. Experimental results show that the accurate recognition rate between semen armeniacae amarum and semen persicae is averaged 99% following the proposed method.

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