scholarly journals Fast automated detection of COVID-19 from medical images using convolutional neural networks

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Shuang Liang ◽  
Huixiang Liu ◽  
Yu Gu ◽  
Xiuhua Guo ◽  
Hongjun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Medical Images ◽  
Clinical Indicators ◽  
Specimen Handling ◽  
Learning Framework ◽  
Computed Tomography Images ◽  
Classification Framework ◽  
Global Pandemic ◽  
The Neural Network ◽  
Diagnosis Method

AbstractCoronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.

scholarly journals Fast automated detection of COVID-19 from medical images using convolutional neural networks

2020 ◽  
Author(s):  
Shuang Liang ◽  
Huixiang Liu ◽  
Yu Gu ◽  
Xiuhua Guo ◽  
Hongjun Li ◽  
...  
Keyword(s):  
Medical Images ◽  
Strong Correlations ◽  
Clinical Indicators ◽  
Learning Framework ◽  
Testing Method ◽  
Classification Framework ◽  
Global Pandemic ◽  
Diagnosis Method ◽  
Aided Diagnosis ◽  
Significant Health

Abstract Coronavirus Disease 2019 (COVID-19) is a global pandemic that poses significant health risks. The sensitivity of diagnostic tests for COVID-19 is low due to irregularities in the handling of the specimens. We propose a deep learning framework that identifies COVID-19 from medical images as an effective auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography (CT) images to train and evaluate the convolutional neural network (CNN). The CNN achieves a performance similar to that of experts and provides high scores for multiple statistical indices, with F1 scores above 96% and specificity over 99%. Heatmaps are used to visualize the salient features extracted by the CNN. The CNN-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, improving the interpretation accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.

scholarly journals Towards Better Forecasting by Fusing Near and Distant Future Visions

2020 ◽  
Vol 34 (04) ◽  
pp. 3593-3600
Author(s):  
Jiezhu Cheng ◽  
Kaizhu Huang ◽  
Zibin Zheng
Keyword(s):  
Neural Network ◽  
Multivariate Time Series ◽  
Predictive Performance ◽  
Distant Future ◽  
Forecasting Accuracy ◽  
Learning Framework ◽  
The Neural Network ◽  
Multi Level ◽  
Real World Datasets ◽  
Future Visions

Multivariate time series forecasting is an important yet challenging problem in machine learning. Most existing approaches only forecast the series value of one future moment, ignoring the interactions between predictions of future moments with different temporal distance. Such a deficiency probably prevents the model from getting enough information about the future, thus limiting the forecasting accuracy. To address this problem, we propose Multi-Level Construal Neural Network (MLCNN), a novel multi-task deep learning framework. Inspired by the Construal Level Theory of psychology, this model aims to improve the predictive performance by fusing forecasting information (i.e., future visions) of different future time. We first use the Convolution Neural Network to extract multi-level abstract representations of the raw data for near and distant future predictions. We then model the interplay between multiple predictive tasks and fuse their future visions through a modified Encoder-Decoder architecture. Finally, we combine traditional Autoregression model with the neural network to solve the scale insensitive problem. Experiments on three real-world datasets show that our method achieves statistically significant improvements compared to the most state-of-the-art baseline methods, with average 4.59% reduction on RMSE metric and average 6.87% reduction on MAE metric.

scholarly journals Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yu Ding ◽  
Qiang Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Kriging Model ◽  
Data Driven ◽  
Power Transformers ◽  
Global Approximation ◽  
The Neural Network ◽  
Modeling Accuracy ◽  
Diagnosis Method ◽  
Transformer Fault

A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil. In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging model with BP neural network is presented and is further extended using linearity weighted aggregation method. The presented method integrates characteristics of the global approximation of the neural network technology and the localized departure of the Kriging model, which improves modeling accuracy. Finally, the validity of this method is demonstrated by several numerical computations of transformer fault diagnosis problems.

scholarly journals Nanoscale neural network using non-linear spin-wave interference

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ádám Papp ◽  
Wolfgang Porod ◽  
Gyorgy Csaba
Keyword(s):  
Neural Network ◽  
Spin Wave ◽  
Spin Waves ◽  
Field Pattern ◽  
Small Scale ◽  
Learning Framework ◽  
Wave Interference ◽  
The Neural Network ◽  
Neural Network Hardware ◽  
Signal Routing

AbstractWe demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic-field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.

Development of Deep Learning Framework for Mathematical Morphology

2019 ◽  
Vol 33 (06) ◽  
pp. 1954024 ◽  
Author(s):  
Frank Y. Shih ◽  
Yucong Shen ◽  
Xin Zhong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Mathematical Morphology ◽  
Experimental Results ◽  
Morphological Operations ◽  
Learning Framework ◽  
Structuring Element ◽  
The Neural Network ◽  
Object Features

Mathematical morphology has been applied as a collection of nonlinear operations related to object features in images. In this paper, we present morphological layers in deep learning framework, namely MorphNet, to perform atomic morphological operations, such as dilation and erosion. For propagation of losses through the proposed deep learning framework, we approximate the dilation and erosion operations by differential and smooth multivariable functions of the softmax function, and therefore enable the neural network to be optimized. The proposed operations are analyzed by the derivative of approximation functions in the deep learning framework. Experimental results show that the output structuring element of a morphological neuron and the target structuring element are matched to confirm the efficiency and correctness of the proposed framework.

The Engine Fault Diagnosis with RBFNN Based on AIC

2012 ◽  
Vol 468-471 ◽  
pp. 1066-1069
Author(s):  
Qiang Huang ◽  
Xiao Zhuo Ouyang ◽  
Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Rbf Neural Network ◽  
Information Criterion ◽  
Vibration Signals ◽  
The Neural Network ◽  
Rotary Machines ◽  
Diagnosis Method ◽  
Diagnosis Model

In this paper, an engine diagnosis method with high precision and quickly response is proposed. Firstly, the Akaike Information Criterion (AIC) is used to improve the performance of the neural network to build the fault diagnosis model. Then the vibration signals are analyzed to estimate the states of the diesel engine. Finally, the five states of diesel engine are set to validate the veracity of diagnosis method. According to experiment and simulation researches, it indicates that the diagnosis method with RBF neural network based on AIC is effective. The veracity of identification is 100% to the single fault. It is a valuable reference to the vibration diagnosis for other complex rotary machines.

scholarly journals A Hybrid Compression Method for Medical Images Based on Region of Interest Using Artificial Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ali Ibrahim Khaleel ◽  
Nik Adilah Hanin Zahri ◽  
Muhammad Imran Ahmad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
State Of The Art ◽  
Medical Images ◽  
Region Of Interest ◽  
Input Image ◽  
Compression Method ◽  
Medical Diagnoses ◽  
The Neural Network ◽  
Similar Images

The number of medical images being stored and communicated daily is rapidly increasing, according to the need for these images in medical diagnoses. Hence, the storage space and bandwidths required to store and communicate these images are exponentially increasing, which has brought attention toward compressing these images. In this study, a new compression method is proposed for medical images based on convolutional neural networks. The proposed neural network consists of two main stages: a segmentation stage and an autoencoder. The segmentation stage is used to recognize the Region of Interest (ROI) in the image and provide it to the autoencoder stage, so more emphasis on the information of the ROI is applied. The autoencoder part of the neural network contains a bottleneck layer that has one-eighth of the dimensions of the input image. The values in this layer are used to represent the image, while the following layers are used to decompress the images, after training the neural network. The proposed method is evaluated using the CLEF MED 2009 dataset, where the evaluation results show that the method has significantly better performance, compared to the existing state-of-the-art methods, by providing more visually similar images using less data.

The Research of Armored Vehicles Weapon System Fault Diagnosis Method

2014 ◽  
Vol 678 ◽  
pp. 309-312
Author(s):  
Hai Feng Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Tree ◽  
Original Data ◽  
Tree Model ◽  
Difficult Situation ◽  
The Neural Network ◽  
Diagnosis Method ◽  
Armored Vehicles ◽  
Neural Network Technology

For armored vehicles electrical system fault diagnosis of fault original data collection difficult situation, this paper introduces the fault diagnosis technology based on fault tree model and fault diagnosis based on neural network technology, and the two kinds of fusion technology, complement each other, with a certain type of equipment control system as an example the case analysis, illustrates the fault tree of the neural network and the rationality and validity of the integrated fault diagnosis thinking.

scholarly journals Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7187
Author(s):  
Chia-Ming Tsai ◽  
Chiao-Sheng Wang ◽  
Yu-Jen Chung ◽  
Yung-Da Sun ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rapid Development ◽  
Sensor Fault ◽  
The Neural Network ◽  
Signal Input ◽  
Diagnosis Method ◽  
The One ◽  
The Time Domain ◽  
Single Neural Network

With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller’s health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.

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