scholarly journals Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fayadh Alenezi ◽  
K. C. Santosh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Network Stability ◽  
Signal To Noise ◽  
Convergence Problem ◽  
Information Fidelity ◽  
Medical Image Enhancement

One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

2021 ◽  
Author(s):  
S.V. Zimina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Useful Signal ◽  
Artificial Neural ◽  
Noise Ratio ◽  
Weight Coefficients

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

scholarly journals Research on Speaker Recognition of DRNN in Different Noise Environment

2020 ◽  
Author(s):  
chaofeng lan ◽  
yuanyuan Zhang ◽  
hongyun Zhao
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Recurrent Neural Network ◽  
Speaker Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Noisy Environment ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Improved Model

Abstract This paper draws on the training method of Recurrent Neural Network (RNN), By increasing the number of hidden layers of RNN and changing the layer activation function from traditional Sigmoid to Leaky ReLU on the input layer, the first group and the last set of data are zero-padded to enhance the effective utilization of data such that the improved reduction model of Denoise Recurrent Neural Network (DRNN) with high calculation speed and good convergence is constructed to solve the problem of low speaker recognition rate in noisy environment. According to this model, the random semantic speech signal with a sampling rate of 16 kHz and a duration of 5 seconds in the speech library is studied. The experimental settings of the signal-to-noise ratios are − 10dB, -5dB, 0dB, 5dB, 10dB, 15dB, 20dB, 25dB. In the noisy environment, the improved model is used to denoise the Mel Frequency Cepstral Coefficients (MFCC) and the Gammatone Frequency Cepstral Coefficents (GFCC), impact of the traditional model and the improved model on the speech recognition rate is analyzed. The research shows that the improved model can effectively eliminate the noise of the feature parameters and improve the speech recognition rate. When the signal-to-noise ratio is low, the speaker recognition rate can be more obvious. Furthermore, when the signal-to-noise ratio is 0dB, the speaker recognition rate of people is increased by 40%, which can be 85% improved compared with the traditional speech model. On the other hand, with the increase in the signal-to-noise ratio, the recognition rate is gradually increased. When the signal-to-noise ratio is 15dB, the recognition rate of speakers is 93%.

Effect of signal to noise ratio on a convolutional neural network for source ranging and environmental classification

2019 ◽  
Vol 146 (4) ◽  
pp. 2961-2962
Author(s):  
Kira Howarth ◽  
David F. Van Komen ◽  
Tracianne B. Neilsen ◽  
David P. Knobles ◽  
Peter H. Dahl ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Environmental Classification ◽  
Noise Ratio

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

2020 ◽  
Vol 20 (03) ◽  
pp. 2050025
Author(s):  
S. Shajun Nisha ◽  
S. P. Raja ◽  
A. Kasthuri
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Signal To Noise ◽  
Coupled Neural Networks ◽  
Noise Ratio ◽  
Pulse Coupled Neural Networks ◽  
Image Quality Index

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.

scholarly journals Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 38 ◽  
Author(s):  
Incheol Kim ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Convolutional Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Input Image ◽  
Visual Interpretation ◽  
Feature Maps ◽  
Novel Method ◽  
Series Of Experiments

Deep learning (DL) methods are increasingly being applied for developing reliable computer-aided detection (CADe), diagnosis (CADx), and information retrieval algorithms. However, challenges in interpreting and explaining the learned behavior of the DL models hinders their adoption and use in real-world systems. In this study, we propose a novel method called “Class-selective Relevance Mapping” (CRM) for localizing and visualizing discriminative regions of interest (ROI) within a medical image. Such visualizations offer improved explanation of the convolutional neural network (CNN)-based DL model predictions. We demonstrate CRM effectiveness in classifying medical imaging modalities toward automatically labeling them for visual information retrieval applications. The CRM is based on linear sum of incremental mean squared errors (MSE) calculated at the output layer of the CNN model. It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image. A series of experiments on a “multi-modality” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods. Further, to visualize its effectiveness we generate “class-specific” ROI maps by averaging the CRM scores of images in each modality class, and characterize the visual explanation through their different size, shape, and location for our multi-modality CNN model that achieved over 98% performance on a dataset constructed from publicly available images.

Application of Constraint CMA Blind Equalization Algorithm in the Medical Image Restoration

2012 ◽  
Vol 263-266 ◽  
pp. 2109-2112
Author(s):  
Jin Zhang ◽  
Ya Jie Mao ◽  
Li Yi Zhang ◽  
Yun Shan Sun
Keyword(s):  
Image Restoration ◽  
Cost Function ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Blind Equalization ◽  
Optimal Solution ◽  
The Novel ◽  
Constant Modulus ◽  
Signal To Noise ◽  
Equalization Algorithm

A constraint constant module blind equalization algorithm for medical image based on dimension reduction was proposed. The constant modulus cost function applied to medical image was founded. In order to improve the effect of image restoration, a constraint item was introduced to restrict cost function, and it guarantees that the algorithm converge the optimal solution. Compared to the traditional methods, the novel algorithm improves peak signal to noise ratio and restoration effects. Computer simulations demonstrate the effectiveness of the algorithm.

An Adaptive Variational PDE Based Image Restoration Model Using Hopfield Neural Network

2011 ◽  
Vol 48-49 ◽  
pp. 174-178
Author(s):  
Wei Sun ◽  
Sheng Nan Liu
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Adaptive Regularization ◽  
Proposed Model ◽  
Degraded Image ◽  
Restoration Problem ◽  
Restoration Model

An adaptive variational partial differential equation (PDE) based aproach for restoration of gray level images degraded by a known shift-invariant blur function and additive noise is presented. The restoration problem of a degraded image is solved by minimizing this model, and this minimizing problem is realized by using Hopfield neural network. In the proposed image restoration model, an adaptive regularization parameter is developed instead of the constant regularization parameter used in previous PDE model. The value of the adaptive regularization parameter changes according to different regions of the image to remove noises and preserve edge better. Several computer simulation results show that the image restoration results of the proposed model both look better and have better SNR (Signal to Noise Ratio) than the previous variational PDE based model.

scholarly journals Face recognition using enhancement discrete wavelet transform based on MATLAB

2021 ◽  
Vol 23 (2) ◽  
pp. 1128
Author(s):  
Asma Abdulelah Abdulrahman ◽  
Fouad Shaker Tahir
Keyword(s):  
Neural Network ◽  
Communication Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
High Quality

<p>In this work, it was proposed to compress the color image after de-noise by proposing a coding for the discrete transport of new wavelets called discrete chebysheve wavelet transduction (DCHWT) and linking it to a neural network that relies on the convolutional neural network to compress the color image. The aim of this work is to find an effective method for face recognition, which is to raise the noise and compress the image in convolutional neural networks to remove the noise that caused the image while it was being transmitted in the communication network. The work results of the algorithm were calculated by calculating the peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP) of the compressed image after a color image (256×256) was entered to demonstrate the quality and efficiency of the proposed algorithm in this work. The result obtained by using a convolutional neural network with new wavelets is to provide a better CR with the ratio of PSNR to be a high value that increases the high-quality ratio of the compressed image to be ready for face recognition.</p>

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