ArticleIT professional2021
Deep Learning-Based COVID-19 Detection Using CT and X-Ray Images: Current Analytics and Comparisons.
Article in IT professional, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
14 citing papers in PubMed.
- Meta-learning for real-world class incremental learning: a transformer-based approach.Scientific reports · 2024Article
- Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions.EJNMMI reports · 2024Review
- RETRACTED ARTICLE: COVID-19 Detection using adopted convolutional neural networks and high-performance computing.Multimedia tools and applications · 2024Article
- Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability.Diagnostics (Basel, Switzerland) · 2023Article
- An Explainable AI-Enabled Framework for Interpreting Pulmonary Diseases from Chest Radiographs.Cancers · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Preliminary Stages for COVID-19 Detection Using Image Processing.Diagnostics (Basel, Switzerland) · 2022Review
- 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 Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type COVID-19 Detection.Frontiers in public health · 2022Article
- A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19.Frontiers in public health · 2022Article
- Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.Microscopy research and technique · 2022Article
- An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach.Microscopy research and technique · 2021Article
- Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types.Microscopy research and technique · 2021Article
- Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM).Environmental technology & innovation · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, the world faces a novel coronavirus disease 2019 (COVID-19) challenge and infected cases are increasing exponentially. COVID-19 is a disease that has been reported by the WHO in March 2020, caused by a virus called the SARS-CoV-2. As of 10 March 2021, more than 150 million people were infected and 3v million died. Researchers strive to find out about the virus and recommend effective actions. An unprecedented increase in pathogens is happening and a major attempt is being made to tackle the epidemic. This article presents deep learning-based COVID-19 detection using CT and X-ray images and data analytics on its spread worldwide. This article's research structure builds on a recent analysis of the COVID-19 data and prospective research to systematize current resources, help the researchers, practitioners by using in-depth learning methodologies to build solutions for the COVID-19 pandemic.
Identifiers
What OpenQuestion holds
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.