Attention-based VGG-16 model for COVID-19 chest X-ray image classification

Applied Intelligence, Journal Year: 2020, Volume and Issue: 51(5), P. 2850 - 2863

Published: Nov. 17, 2020

Computer-aided diagnosis (CAD) methods such as Chest X-rays (CXR)-based method is one of the cheapest alternative options to diagnose early stage COVID-19 disease compared other alternatives Polymerase Chain Reaction (PCR), Computed Tomography (CT) scan, and so on. To this end, there have been few works proposed by using CXR-based methods. However, they limited performance ignore spatial relationships between region interests (ROIs) in CXR images, which could identify likely regions COVID-19's effect human lungs. In paper, we propose a novel attention-based deep learning model attention module with VGG-16. By module, capture relationship ROIs images. meantime, an appropriate convolution layer (4th pooling layer) VGG-16 addition design perform fine-tuning classification process. evaluate our method, conduct extensive experiments three image datasets. The experiment analysis demonstrate stable promising state-of-the-art indicates that it suitable for diagnosis.

Language: Английский

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Diagnosis of COVID-19 using CT scan images and deep learning techniques

Emergency Radiology, Journal Year: 2021, Volume and Issue: 28(3), P. 497 - 505

Published: Feb. 1, 2021

Language: Английский

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A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 30551 - 30572

Published: Jan. 1, 2021

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and become one of most acute severe ailments in past hundred years. The prevalence rate COVID-19 is rapidly rising every day throughout globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be powerful tool arsenal used by clinicians automatic diagnosis COVID-19. This paper aims overview recently developed systems based on using different medical imaging modalities like Computer Tomography (CT) X-ray. review specifically discusses provides insights well-known data sets train these networks. It also highlights partitioning various performance measures researchers field. A taxonomy drawn categorize recent works proper insight. Finally, we conclude addressing challenges associated with use methods detection probable future trends research area. aim facilitate experts (medical or otherwise) technicians understanding ways are regard how they can potentially further utilized combat outbreak

Language: Английский

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Transfer learning techniques for medical image analysis: A review

Journal of Applied Biomedicine, Journal Year: 2021, Volume and Issue: 42(1), P. 79 - 107

Published: Dec. 13, 2021

Language: Английский

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Applications of artificial intelligence in battling against covid-19: A literature review

Chaos Solitons & Fractals, Journal Year: 2020, Volume and Issue: 142, P. 110338 - 110338

Published: Oct. 3, 2020

Language: Английский

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Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Mathematical Problems in Engineering, Journal Year: 2021, Volume and Issue: 2021, P. 1 - 9

Published: Feb. 25, 2021

COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans 2019. Coronaviruses cause range of illness patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East (MERS-CoV). The SARS-CoV-2 outbreak has resulted global pandemic, its transmission increasing at rapid rate. Diagnostic testing approaches provide valuable tool for doctors support them with screening process. Automatic identification chest X-ray images can be useful test infection good speed. Therefore, this paper, framework designed using Convolutional Neural Networks (CNN) diagnose images. A pretrained GoogLeNet utilized implementing transfer learning (i.e., replacing some sets final network CNN layers). 20-fold cross-validation considered overcome overfitting quandary. Finally, multiobjective genetic algorithm tune hyperparameters proposed Extensive experiments show model obtains remarkably better results may real-time patients.

Language: Английский

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Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

Pattern Analysis and Applications, Journal Year: 2021, Volume and Issue: 24(3), P. 1111 - 1124

Published: March 19, 2021

COVID-19 continues to have catastrophic effects on the lives of human beings throughout world. To combat this disease it is necessary screen affected patients in a fast and inexpensive way. One most viable steps towards achieving goal through radiological examination, Chest X-Ray being easily available least expensive option. In paper, we proposed Deep Convolutional Neural Network-based solution which can detect +ve using chest images. Multiple state-of-the-art CNN models—DenseNet201, Resnet50V2 Inceptionv3, been adopted work. They trained individually make independent predictions. Then models are combined, new method weighted average ensembling technique, predict class value. test efficacy used publicly X-ray images COVID –ve cases. 538 468 divided into training, validation sets. The approach gave classification accuracy 91.62% higher than as well compared benchmark algorithm. We developed GUI-based application for public use. This be any computer by medical personnel within few seconds.

Language: Английский

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Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

Computational and Mathematical Methods in Medicine, Journal Year: 2020, Volume and Issue: 2020, P. 1 - 10

Published: Sept. 26, 2020

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. last few months have witnessed rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose with computed tomography (CT). In this study, we review diagnosis by using CT toward AI. We searched ArXiv, MedRxiv, Google Scholar terms “deep learning”, “neural networks”, “COVID-19”, “chest CT”. At time writing (August 24, 2020), there been nearly 100 30 among them were selected for review. categorized based on classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, severity. sensitivity, specificity, precision, accuracy, area under curve, F1 score results reported as high 100%, 99.62, 99.87%, 99.5%, respectively. However, presented should be carefully compared due different degrees difficulty tasks.

Language: Английский

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Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Expert Systems with Applications, Journal Year: 2021, Volume and Issue: 185, P. 115695 - 115695

Published: Aug. 4, 2021

Language: Английский

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Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Diagnostics, Journal Year: 2021, Volume and Issue: 11(4), P. 607 - 607

Published: March 29, 2021

Chronic diseases are becoming more widespread. Treatment and monitoring of these require going to hospitals frequently, which increases the burdens patients. Presently, advancements in wearable sensors communication protocol contribute enriching healthcare system a way that will reshape services shortly. Remote patient (RPM) is foremost advancements. RPM systems based on collection vital signs extracted using invasive noninvasive techniques, then sending them real-time physicians. These data may help physicians taking right decision at time. The main objective this paper outline research directions remote monitoring, explain role AI building systems, make an overview state art RPM, its advantages, challenges, probable future directions. For studying literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, science.gov). We followed (Preferred Reporting Items for Systematic Reviews Meta-Analyses) PRISMA, standard methodology systematic reviews meta-analyses. A total 56 articles reviewed combination set selected search terms including mining, clinical support system, electronic health record, cloud computing, internet things, wireless body area network. result study approved effectiveness improving delivery, increase diagnosis speed, reduce costs. To end, we also present chronic disease as case provide enhanced solutions RPMs.

Language: Английский

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