Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text DOI Creative Commons
Shashi Shekhar, Hitendra Garg, Rohit Agrawal

et al.

Complex & Intelligent Systems, Journal Year: 2021, Volume and Issue: 9(3), P. 2813 - 2826

Published: Aug. 17, 2021

Abstract The paper describes the usage of self-learning Hierarchical LSTM technique for classifying hatred and trolling contents in social media code-mixed data. LSTM-based learning is a novel architecture inspired from neural models. proposed HLSTM model trained to identify words available contents. systems equipped with predicting mechanism annotating transliteration domain. Hindi–English data are ordered into Hindi, English, labels classification. word embedding character-embedding features used here representation sentence detect words. method developed based on helps recognizing context by mining intention user using that sentence. Wide experiments suggests HLSTM-based classification gives accuracy 97.49% when evaluated against standard parameters like BLSTM, CRF, LR, SVM, Random Forest Decision Tree models especially there some

Language: Английский

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal DOI Creative Commons
Laure Wynants, Ben Van Calster, Gary S. Collins

et al.

BMJ, Journal Year: 2020, Volume and Issue: unknown, P. m1328 - m1328

Published: April 7, 2020

To review and appraise the validity usefulness of published preprint reports prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, prognosis covid-19, detecting people general population at increased risk covid-19 infection or being admitted to hospital disease.

Language: Английский

Citations

2836
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks DOI Creative Commons
Ali Narin, Ceren Kaya, Ziynet Pamuk

et al.

Pattern Analysis and Applications, Journal Year: 2021, Volume and Issue: 24(3), P. 1207 - 1220

Published: May 9, 2021

Language: Английский

Citations

1305
A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images DOI Creative Commons

Md. Zabirul Islam,

Md. Milon Islam, Amanullah Asraf

et al.

Informatics in Medicine Unlocked, Journal Year: 2020, Volume and Issue: 20, P. 100412 - 100412

Published: Jan. 1, 2020

Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An framework assists doctors the diagnosis of and provides exact, consistent, fast results reduces death rate. Coronavirus (COVID-19) one most severe acute diseases recent times spread globally. Therefore, an automated system, as fastest diagnostic option, should be implemented impede COVID-19 from spreading. This paper aims introduce deep learning technique based on combination convolutional neural network (CNN) long short-term memory (LSTM) diagnose automatically X-ray images. In this CNN is used for feature extraction LSTM using extracted feature. A collection 4575 images, including 1525 images COVID-19, were dataset system. The experimental show that our proposed system achieved accuracy 99.4%, AUC 99.9%, specificity 99.2%, sensitivity 99.3%, F1-score 98.9%. desired currently available dataset, which can further improved when more available. help treat patients easily.

Language: Английский

Citations

568
Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning DOI
Aayush Jaiswal, Neha Gianchandani, Dilbag Singh

et al.

Journal of Biomolecular Structure and Dynamics, Journal Year: 2020, Volume and Issue: 39(15), P. 5682 - 5689

Published: July 3, 2020

Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train weights networks on large datasets as well fine tuning pre-trained small datasets. Due to COVID-19 dataset available, neural be for diagnosis coronavirus. However, these applied chest CT image is very limited till now. Hence, main aim this paper use deep architectures an automated tool detection and CT. A DenseNet201 based transfer (DTL) proposed classify patients COVID infected or not i.e. (+) (−). The model utilized extract features by using its own learned ImageNet along with a convolutional structure. Extensive experiments performed evaluate performance propose DTL scan Comparative analyses reveal that classification outperforms competitive approaches.

Language: Английский

Citations

563
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation DOI Creative Commons
Amine Amyar, Romain Modzelewski,

Hua Li

et al.

Computers in Biology and Medicine, Journal Year: 2020, Volume and Issue: 126, P. 104037 - 104037

Published: Oct. 8, 2020

Language: Английский

Citations

506
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data DOI Creative Commons
Michael J. Horry, Subrata Chakraborty, Manoranjan Paul

et al.

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 149808 - 149824

Published: Jan. 1, 2020

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep models can be used to perform detection using images three most commonly medical imaging modes X-Ray, Ultrasound, CT scan. The aim is provide over-stressed professionals a second pair of eyes through intelligent image classification models. We identify suitable Convolutional Neural Network (CNN) model initial comparative study several popular CNN then optimize the selected VGG19 for modalities show highly scarce challenging datasets. highlight challenges (including dataset size quality) utilizing current publicly available datasets developing useful it adversely impacts trainability complex also propose pre-processing stage create trustworthy testing new approach aimed reduce unwanted noise so that focus on detecting diseases with specific features them. Our results indicate Ultrasound superior accuracy compared X-Ray scans. experimental limited data, deeper networks struggle train well provides less consistency over are using. model, which extensively tuned parameters, performs considerable levels against pneumonia or normal all lung precision up 86% 100% 84%

Language: Английский

Citations

437
Deep Transfer Learning Based Classification Model for COVID-19 Disease DOI Open Access
Yadunath Pathak, Prashant Kumar Shukla, Akhilesh Tiwari

et al.

IRBM, Journal Year: 2020, Volume and Issue: 43(2), P. 87 - 92

Published: May 20, 2020

Language: Английский

Citations

417
Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices DOI Creative Commons

Sakshi Ahuja,

Bijaya Ketan Panigrahi, Nilanjan Dey

et al.

Applied Intelligence, Journal Year: 2020, Volume and Issue: 51(1), P. 571 - 585

Published: Aug. 21, 2020

Language: Английский

Citations

322
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays DOI Open Access
Nripendra Narayan Das, Naresh Kumar,

Manjit Kaur

et al.

IRBM, Journal Year: 2020, Volume and Issue: 43(2), P. 114 - 119

Published: July 3, 2020

Language: Английский

Citations

319
A deep learning approach to detect Covid-19 coronavirus with X-Ray images DOI Open Access

Govardhan Jain,

Deepti Mittal,

Daksh Thakur

et al.

Journal of Applied Biomedicine, Journal Year: 2020, Volume and Issue: 40(4), P. 1391 - 1405

Published: Sept. 7, 2020

Language: Английский

Citations

294