ArticleIEEE internet of things journal2021
An IoT-Based Deep Learning Framework for Early Assessment of Covid-19.
Article in IEEE internet of things journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.Frontiers in public health · 2022Pooled it
- Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network.Scientific reports · 2024Article
- A deep convolutional neural network approach using medical image classification.BMC medical informatics and decision making · 2024Article
- A Topical Review on Enabling Technologies for the Internet of Medical Things: Sensors, Devices, Platforms, and Applications.Micromachines · 2024Review
- IoT-based COVID-19 detection using recalling-enhanced recurrent neural network optimized with golden eagle optimization algorithm.Medical & biological engineering & computing · 2024Article
- Automatic detection of small bowel lesions with different bleeding risks based on deep learning models.World journal of gastroenterology · 2024Article
- MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.Information sciences · 2023Article
- A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer.Sensors (Basel, Switzerland) · 2023Article
- DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Survival study on deep learning techniques for IoT enabled smart healthcare system.Health and technology · 2023Article
- COVICT: an IoT based architecture for COVID-19 detection and contact tracing.Journal of ambient intelligence and humanized computing · 2023Article
- Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.Sensors (Basel, Switzerland) · 2022Review
- Mammogram Image Enhancement Techniques for Online Breast Cancer Detection and Diagnosis.Sensors (Basel, Switzerland) · 2022Article
- ADL-CDF: A Deep Learning Framework for COVID-19 Detection from CT Scans Towards an Automated Clinical Decision Support System.Arabian journal for science and engineering · 2022Article
- A sustainable advanced artificial intelligence-based framework for analysis of COVID-19 spread.Environment, development and sustainability · 2022Article
- Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses.Interdisciplinary sciences, computational life sciences · 2022Article
- Software system to predict the infection in COVID-19 patients using deep learning and web of things.Software: practice & experience · 2022Article
- Ensemble Deep Learning and Internet of Things-Based Automated COVID-19 Diagnosis Framework.Contrast media & molecular imaging · 2022Article
- Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model.The Journal of supercomputing · 2022Article
- Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancement in the Internet of Medical Things (IoMT), along with machine learning, deep learning, and artificial intelligence techniques, initiated a world of possibilities in healthcare. It has an extensive range of applications: when connected to the Internet, ordinary medical devices and sensors can collect valuable data, deep learning, and artificial intelligence techniques utilize this data and give an insight of symptoms, trends and enable remote care. Recently, Covid-19 pandemic outbreak caused the death of a large number of people. This virus has infected millions of people, and still, the rate of infected people is increasing day by day. Researchers are endeavoring to utilize medical images and deep learning-based models for the detection of Covid-19. Various techniques have been presented that utilize X-Ray images of the chest for the detection of Covid-19. However, the importance of regional-based convolutional neural networks (CNNs) is currently confined. Thus, this research aimed to introduce an IoT-based deep learning framework for early assessment of Covid-19. This framework can reduce the working pressure of medical experts/radiologists and contribute to the pandemic control. A deep learning-based model, i.e., faster regions with CNNs (Faster-RCNN) with ResNet-101, is applied on X-Ray images of the chest for Covid-19 detection. It uses region proposal network (RPN) to perform detection. By employing the model, we achieve a detection accuracy of 98%. Therefore, we believe that the system might be capable in order to assist medical expert/radiologist, to verify early assessment toward Covid-19.
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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.