ArticleAn international journal on information fusion2021
COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images.
Article in An international journal on information fusion, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- AMFF-Net: An Adaptive Multi-Layer Feature Fusion Network Based on ConvNeXtV2-B for Medical Image Classification.Bioengineering (Basel, Switzerland) · 2026Article
- MFFC-Net: Multi-feature Fusion Deep Networks for Classifying Pulmonary Edema of a Pilot Study by Using Lung Ultrasound Image with Texture Analysis and Transfer Learning Technique.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Thoracic Surgery: Transforming Diagnostics, Treatment, and Patient Outcomes.Diagnostics (Basel, Switzerland) · 2025Review
- Current Diagnostic Techniques for Pneumonia: A Scoping Review.Sensors (Basel, Switzerland) · 2024Article
- Efficient Lung Ultrasound Classification.Bioengineering (Basel, Switzerland) · 2023Article
- Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review.Multimedia tools and applications · 2023Article
- Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network.Journal of healthcare engineering · 2023Article
- Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.Multimedia systems · 2023Article
- Deep learning application detecting SARS-CoV-2 key enzymes inhibitors.Cluster computing · 2023Article
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
- IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database.Healthcare (Basel, Switzerland) · 2023Article
- Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography.Informatics in medicine unlocked · 2023Article
- COVID-19-The Role of Artificial Intelligence, Machine Learning, and Deep Learning: A Newfangled.Archives of computational methods in engineering : state of the art reviews · 2023Review
- DDiagnostics (Basel, Switzerland) · 2022Article
- Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion.Diagnostics (Basel, Switzerland) · 2022Article
- E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.Engineering applications of artificial intelligence · 2022Article
- Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.Cluster computing · 2022Article
- Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.