Low-Noise Magnetic Coil System for Recording 3-D Eye Movements

Hageman, Kristin N.; Chow, Margaret R.; Roberts, Dale; Santina, Charles C. Della

doi:10.1109/tim.2020.3020682

Cited by 258 publications

(93 citation statements)

References 22 publications

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“…The convolution neural network (CNN) is used to identify MI signals in an efficient computer-aided diagnosis (CAD) framework for urban healthcare in smart cities [ 12 ]. The novel feature extraction protocol followed by the genetic algorithm is proposed to detect arrhythmia to improve the performance using several tiers [ 13 ]. The structure is as follows: Section 2 discusses the related work connected with cervical screening, Section 3 elaborates the proposed architecture of CYENET to cervical screening, Section 4 interprets the results obtained out of the implementation, and Section 5 drawn the conclusion and future scope of this work.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Venkatesan

Sumithra

Karthick

et al. 2021

BioMed Research International

119

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Traditional screening of cervical cancer type classification majorly depends on the pathologist’s experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Venkatesan

Sumithra

Karthick

et al. 2021

BioMed Research International

119

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“…In order to verify the validity of the model, some models [ 39 – 42 ] use statistical testing methods to ensure the superiority of the model. Common indicators used to evaluate the accuracy of prediction models in structural deformation prediction models include mean square error (MSE), root mean square error (RMSE) [ 43 ], mean absolute error (MAE), and mean absolute percentage error (MAPE). RMSE is very sensitive to large or small errors and can well reflect the measurement precision.…”

Section: Experimental Results and Performance Analysismentioning

confidence: 99%

A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks

Luo

Gan

Wang

et al. 2021

Computational Intelligence and Neuroscience

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The structural engineering is subject to various subjective and objective factors, the deformation is usually inevitable, the deformation monitoring data usually are nonstationary and nonlinear, and the deformation prediction is a difficult problem in the field of structural monitoring. Aiming at the problems of the traditional structural deformation prediction methods, a structural deformation prediction model is proposed based on temporal convolutional networks (TCNs) in this study. The proposed model uses a one-dimensional dilated causal convolution to reduce the model parameters, expand the receptive field, and prevent future information leakage. By obtaining the long-term memory of time series, the internal time characteristics of structural deformation data can be effectively mined. The network hyperparameters of the TCN model are optimized by the orthogonal experiment, which determines the optimal combination of model parameters. The experimental results show that the predicted values of the proposed model are highly consistent with the actual monitored values. The average RMSE, MAPE, and MAE with the optimized model parameters reduce 44.15%, 82.03%, and 66.48%, respectively, and the average running time is reduced by 45.41% compared with the results without optimization parameters. The average RMSE, MAE, and MAPE reduce by 26.88%, 62.16%, and 40.83%, respectively, compared with WNN, DBN-SVR, GRU, and LSTM models.

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“…Image-to-image translation [ 34 ] is an important application of GANs. This has been applied to many tasks, such as super-resolution [ 35 ], binarization [ 36 , 37 ], document analysis [ 38 , 39 ], etc. In addition to the above, many CNN based approaches have also been proposed for COVID-19 detection in radiological images.…”

Section: Related Workmentioning

confidence: 99%

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Karbhari¹,

Basu

Geem

et al. 2021

Diagnostics

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COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.

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Low-Noise Magnetic Coil System for Recording 3-D Eye Movements

Cited by 258 publications

References 22 publications

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

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