COVID-19 image classification using deep learning: Advances, challenges and opportunities

Computers in Biology and Medicine, Год журнала: 2022, Номер 144, С. 105350 - 105350

Опубликована: Март 3, 2022

Язык: Английский

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An IoT-Based Deep Learning Framework for Early Assessment of Covid-19

IEEE Internet of Things Journal, Год журнала: 2020, Номер 8(21), С. 15855 - 15862

Опубликована: Окт. 27, 2020

Advancement in the Internet of Medical Things (IoMT), along with machine learning, deep and artificial intelligence techniques, initiated a world possibilities healthcare. It has an extensive range applications: when connected to Internet, ordinary medical devices sensors can collect valuable data, techniques utilize this data give insight symptoms, trends enable remote care. Recently, Covid-19 pandemic outbreak caused death large number people. This virus infected millions people, still, rate people is increasing day by day. Researchers are endeavoring images learning-based models for detection Covid-19. Various have been presented that X-Ray chest However, importance regional-based convolutional neural networks (CNNs) currently confined. Thus, research aimed introduce IoT-based learning framework early assessment reduce working pressure experts/radiologists contribute control. A model, i.e., faster regions CNNs (Faster-RCNN) ResNet-101, applied on detection. uses region proposal network (RPN) perform By employing we achieve accuracy 98%. Therefore, believe system might be capable order assist expert/radiologist, verify toward

Язык: Английский

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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

Health and Technology, Год журнала: 2021, Номер 11(2), С. 411 - 424

Опубликована: Фев. 5, 2021

The scientific community has joined forces to mitigate the scope of current COVID-19 pandemic. early identification disease, as well evaluation its evolution is a primary task for timely application medical protocols. use images chest provides valuable information specialists. Specifically, X-ray have been focus many investigations that apply artificial intelligence techniques automatic classification this disease. results achieved date on subject are promising. However, some these contain errors must be corrected obtain appropriate models clinical use. This research discusses problems found in literature COVID-19. It evident most reviewed works an incorrect protocol applied, which leads overestimating results.

Язык: Английский

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Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook

The Science of The Total Environment, Год журнала: 2021, Номер 803, С. 149834 - 149834

Опубликована: Авг. 21, 2021

Язык: Английский

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COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN

Diagnostics, Год журнала: 2022, Номер 12(2), С. 267 - 267

Опубликована: Янв. 21, 2022

COVID-19 is a respiratory illness that has affected large population worldwide and continues to have devastating consequences. It imperative detect at the earliest opportunity limit span of infection. In this work, we developed new CNN architecture STM-RENet interpret radiographic patterns from X-ray images. The proposed block-based employs idea split-transform-merge in way. regard, convolutional block STM implements region edge-based operations separately, as well jointly. systematic use edge implementations combination with helps exploring homogeneity, intensity inhomogeneity, boundary-defining features. learning capacity further enhanced by developing CB-STM-RENet exploits channel boosting learns textural variations effectively screen images exploited generating auxiliary channels two additional CNNs using Transfer Learning, which are then concatenated original STM-RENet. A significant performance improvement shown comparison standard on three datasets, especially stringent CoV-NonCoV-15k dataset. good detection rate (97%), accuracy (96.53%), reasonable F-score (95%) technique suggest it can be adapted infected patients.

Язык: Английский

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COVID-19 classification of X-ray images using deep neural networks

European Radiology, Год журнала: 2021, Номер 31(12), С. 9654 - 9663

Опубликована: Май 29, 2021

In the midst of coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring patients with COVID-19. We propose a deep learning model for detection COVID-19 from CXRs, as well tool retrieving similar according to model's results on their CXRs. For training evaluating our model, we collected CXRs inpatients hospitalized four different hospitals.In this retrospective study, 1384 frontal confirmed imaged between March August 2020, 1024 matching non-COVID before pandemic, were used build classifier detecting positive The consists ensemble pre-trained neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, vgg16, enhanced by data augmentation lung segmentation. further implemented nearest-neighbors algorithm that uses DNN-based image embeddings retrieve images most given image.Our achieved accuracy 90.3%, (95% CI: 86.3-93.7%) specificity 90% 84.3-94%), sensitivity 90.5% 85-94%) test dataset comprising 15% (350/2326) original images. AUC ROC curve 0.96 0.93-0.97).We provide models, trained evaluated can assist medical efforts reduce staff workload handling COVID-19.• A machine was able detect tested rate above 90%. • created finding existing CXR characteristics CXR, embeddings.

Язык: Английский

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Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey

SN Computer Science, Год журнала: 2022, Номер 3(4)

Опубликована: Май 12, 2022

Язык: Английский

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Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images

Pattern Recognition, Год журнала: 2022, Номер 131, С. 108826 - 108826

Опубликована: Июнь 6, 2022

Язык: Английский

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Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

Bioengineering, Год журнала: 2023, Номер 10(3), С. 363 - 363

Опубликована: Март 16, 2023

Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures chest X-rays computed tomography (CT) scans. Deep learning (DL) artificial intelligence (AI) are critical tools for early accurate detection of COVID-19. This research explores the different DL techniques identifying pneumonia on medical CT radiography images using ResNet152, VGG16, ResNet50, DenseNet121. The ResNet framework uses scan with accuracy precision. automates optimum model architecture training parameters. Transfer approaches also employed solve content gaps shorten duration. An upgraded VGG16 deep transfer is applied perform multi-class classification X-ray imaging tasks. Enhanced has proven recognize three types radiographic 99% accuracy, typical pneumonia. validity performance metrics proposed were validated publicly available data sets. suggested outperforms competing in diagnosing primary outcomes this result an average F-score (95%, 97%). In event healthy viral infections, more efficient than existing methodologies coronavirus detection. created appropriate recognition pre-training. traditional strategies categorization illnesses.

Язык: Английский

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Dual-path information enhanced pyramid Unet for COVID-19 lung infection segmentation

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 142, С. 109977 - 109977

Опубликована: Янв. 5, 2025

Язык: Английский

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