ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2021
Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 78 citations in OpenAlex.
- Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review.JMIR mHealth and uHealth · 2024Pooled 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
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Application of Generative Adversarial Networks to Improve COVID-19 Classification on Ultrasound Images.Journal of imaging · 2025Article
- Global Trends and Hotspots in Ultrasound Imaging for Rehabilitation Research: A Bibliometric and Visualization Analysis.Journal of multidisciplinary healthcare · 2025Article
- Article
- An Interpretable Neonatal Lung Ultrasound Feature Extraction and Lung Sliding Detection System Using Object Detectors.IEEE journal of translational engineering in health and medicine · 2024Article
- Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?-Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Automated Video-Based Capture of Crustacean Fisheries Data Using Low-Power Hardware.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.IEEE journal of biomedical and health informatics · 2023Article
- HQDCNet: Hybrid Quantum Dilated Convolution Neural Network for detecting covid-19 in the context of Big Data Analytics.Multimedia tools and applications · 2023Article
- Efficient Lung Ultrasound Classification.Bioengineering (Basel, Switzerland) · 2023Article
- A Systematic Literature Review and Future Perspectives for Handling Big Data Analytics in COVID-19 Diagnosis.New generation computing · 2023Article
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- GUI Enabled Optimized Approach of CNN for Automatic Diagnosis of COVID-19 Using Radiograph Images.New generation computing · 2023Article
- Bandwidth Improvement in Ultrasound Image Reconstruction Using Deep Learning Techniques.Healthcare (Basel, Switzerland) · 2022Article
- State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses.Ultrasound in medicine & biology · 2022Review
- Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery.Journal of personalized medicine · 2022Article
- Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic.Journal of imaging · 2022Review
- A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients.BME frontiers · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with other lightweight neural network models along with state-of-the-art heavy model. It was shown that the proposed network can achieve the highest accuracy of 83.2% and requires a training time of only 24 min. The proposed Mini-COVIDNet has 4.39 times less number of parameters in the network compared to its next best performing network and requires a memory of only 51.29 MB, making the point-of-care detection of COVID-19 using lung US imaging plausible on a mobile platform. Deployment of these lightweight networks on embedded platforms shows that the proposed Mini-COVIDNet is highly versatile and provides optimal performance in terms of being accurate as well as having latency in the same order as other lightweight networks. The developed lightweight models are available at https://github.com/navchetan-awasthi/Mini-COVIDNet.
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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.