scholarly journals COVID-19 Detection System Using Chest CT Images and Multiple Kernels-Extreme Learning Machine Based on Deep Neural Network

IRBM ◽  
2021 ◽  
Author(s):  
M. Turkoglu
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Deep Neural Network ◽  
Detection System ◽  
Ct Images ◽  
Chest Ct ◽  
Learning Machine

Deep Transfer Convolutional Neural Network and Extreme Learning Machine for lung nodule diagnosis on CT images

2020 ◽  
Vol 204 ◽  
pp. 106230 ◽  
Author(s):  
Xufeng Huang ◽  
Qiang Lei ◽  
Tingli Xie ◽  
Yahui Zhang ◽  
Zhen Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Extreme Learning Machine ◽  
Lung Nodule ◽  
Ct Images ◽  
Learning Machine

scholarly journals Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks

2020 ◽  
Author(s):  
Bin Liu ◽  
Xiaoxue Gao ◽  
Mengshuang He ◽  
Fengmao Lv ◽  
Guosheng Yin
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Area Under The Curve ◽  
Ct Images ◽  
Chest Ct ◽  
Diagnostic Tools ◽  
Ct Image ◽  
Medical Systems ◽  
Negative Case

Chest computed tomography (CT) scanning is one of the most important technologies for COVID-19 diagnosis and disease monitoring, particularly for early detection of coronavirus. Recent advancements in computer vision motivate more concerted efforts in developing AI-driven diagnostic tools to accommodate the enormous demands for the COVID-19 diagnostic tests globally. To help alleviate burdens on medical systems, we develop a lesion-attention deep neural network (LA-DNN) to predict COVID-19 positive or negative with a richly annotated chest CT image dataset. Based on the textual radiological report accompanied with each CT image, we extract two types of important information for the annotations: One is the indicator of a positive or negative case of COVID-19, and the other is the description of five lesions on the CT images associated with the positive cases. The proposed data-efficient LA-DNN model focuses on the primary task of binary classification for COVID-19 diagnosis, while an auxiliary multi-label learning task is implemented simultaneously to draw the model's attention to the five lesions associated with COVID-19. The joint task learning process makes it a highly sample-efficient deep neural network that can learn COVID-19 radiology features more effectively with limited but high-quality, rich-information samples. The experimental results show that the area under the curve (AUC) and sensitivity (recall), precision, and accuracy for COVID-19 diagnosis are 94.0%, 88.8%, 87.9%, and 88.6% respectively, which reach the clinical standards for practical use. A free online system is currently alive for fast diagnosis using CT images at the website https://www.covidct.cn/, and all codes and datasets are freely accessible at our github address.

Generalized extreme learning machine autoencoder and a new deep neural network

2017 ◽  
Vol 230 ◽  
pp. 374-381 ◽  
Author(s):  
Kai Sun ◽  
Jiangshe Zhang ◽  
Chunxia Zhang ◽  
Junying Hu
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Deep Neural Network ◽  
Learning Machine

scholarly journals Data-Driven Approach for Rainfall-Runoff Modelling Using Equilibrium Optimizer Coupled Extreme Learning Machine and Deep Neural Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6238
Author(s):  
Bishwajit Roy ◽  
Maheshwari Prasad Singh ◽  
Mosbeh R. Kaloop ◽  
Deepak Kumar ◽  
Jong-Wan Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Deep Neural Network ◽  
Optimal Number ◽  
Gradient Boosting ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Rainfall Runoff ◽  
Hydrological Variable ◽  
Learning Machine

Rainfall-runoff (R-R) modelling is used to study the runoff generation of a catchment. The quantity or rate of change measure of the hydrological variable, called runoff, is important for environmental scientists to accomplish water-related planning and design. This paper proposes (i) an integrated model namely EO-ELM (an integration of equilibrium optimizer (EO) and extreme learning machine (ELM)) and (ii) a deep neural network (DNN) for one day-ahead R-R modelling. The proposed R-R models are validated at two different benchmark stations of the catchments, namely river Teifi at Glanteifi and river Fal at Tregony in the UK. Firstly, a partial autocorrelation function (PACF) is used for optimal number of lag inputs to deploy the proposed models. Six other well-known machine learning models, called ELM, kernel ELM (KELM), and particle swarm optimization-based ELM (PSO-ELM), support vector regression (SVR), artificial neural network (ANN) and gradient boosting machine (GBM) are utilized to validate the two proposed models in terms of prediction efficiency. Furthermore, to increase the performance of the proposed models, paper utilizes a discrete wavelet-based data pre-processing technique is applied in rainfall and runoff data. The performance of wavelet-based EO-ELM and DNN are compared with wavelet-based ELM (WELM), KELM (WKELM), PSO-ELM (WPSO-ELM), SVR (WSVR), ANN (WANN) and GBM (WGBM). An uncertainty analysis and two-tailed t-test are carried out to ensure the trustworthiness and efficacy of the proposed models. The experimental results for two different time series datasets show that the EO-ELM performs better in an optimal number of lags than the others. In the case of wavelet-based daily R-R modelling, proposed models performed better and showed robustness compared to other models used. Therefore, this paper shows the efficient applicability of EO-ELM and DNN in R-R modelling that may be used in the hydrological modelling field.

Sign in / Sign up

Export Citation Format

Share Document