DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

SN Computer Science, Journal Year: 2021, Volume and Issue: 2(5)

Published: July 23, 2021

Language: Английский

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Densely attention mechanism based network for COVID-19 detection in chest X-rays

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Jan. 6, 2023

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities widespread ground-glass opacities. This makes automatic recognition imaging challenging task. To overcome this issue, we propose densely attention mechanism-based network (DAM-Net) CXR. DAM-Net adaptively extracts spatial from infected regions with various appearances scales. Our proposed is composed dense layers, channel adaptive downsampling layer, label smoothing regularization loss function. Dense layers extract approach builds up weights major feature channels suppresses redundant representations. We use cross-entropy function based on to limit effect interclass similarity upon The trained tested largest publicly available dataset, i.e., COVIDx, consisting 17,342 CXRs. Experimental results demonstrate that obtains state-of-the-art classification an accuracy 97.22%, sensitivity 96.87%, specificity 99.12%, precision 95.54%.

Language: Английский

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Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 133, P. 109906 - 109906

Published: Dec. 7, 2022

Language: Английский

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Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture

SN Computer Science, Journal Year: 2023, Volume and Issue: 4(3)

Published: Feb. 17, 2023

Language: Английский

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Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3267 - 3301

Published: Feb. 19, 2024

Language: Английский

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Study on transfer learning capabilities for pneumonia classification in chest-x-rays images

Computer Methods and Programs in Biomedicine, Journal Year: 2022, Volume and Issue: 221, P. 106833 - 106833

Published: April 23, 2022

Language: Английский

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RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 204, P. 117410 - 117410

Published: April 27, 2022

Language: Английский

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Detection of SARS-CoV-2 Virus Using Lightweight Convolutional Neural Networks

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: 135(2), P. 941 - 965

Published: March 1, 2024

Language: Английский

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Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

Computational Intelligence and Neuroscience, Journal Year: 2021, Volume and Issue: 2021(1)

Published: Jan. 1, 2021

The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for new generation of aircraft engines. With the increase massive monitoring data, it brings opportunities to improve prediction from perspective deep learning. Therefore, we propose novel joint learning architecture that composed two main parts: transformer encoder, which uses scaled dot-product attention extract dependencies across distances time series, temporal convolution neural network (TCNN), constructed fix insensitivity self-attention mechanism local features. Both parts are jointly trained within regression module, implies proposed approach differs traditional ensemble models. It applied on Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset Prognostics Center Excellence at NASA Ames, satisfactory results obtained, especially under complex working conditions.

Language: Английский

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Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Computers, Journal Year: 2023, Volume and Issue: 12(5), P. 95 - 95

Published: May 2, 2023

Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of chest emerged as a valuable and cost-effective tool for detecting diagnosing patients. In this study, we developed deep learning model using transfer with optimized DenseNet-169 DenseNet-201 models three-class classification, utilizing Nadam optimizer. We modified traditional DenseNet architecture tuned hyperparameters improve model’s performance. The was evaluated novel dataset 3312 images from publicly available datasets, metrics such accuracy, recall, precision, F1-score, area under receiver operating characteristics curve. Our results showed impressive detection rate accuracy recall patients, 95.98% 96% achieved 96.18% 99% DenseNet-201. Unique layer configurations optimization algorithm enabled our achieve rates not only patients but also identifying normal pneumonia-affected ability detect lung problems early on, well low false-positive false-negative rates, suggest that it potential serve reliable diagnostic variety diseases.

Language: Английский

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