ArticleComputational intelligence and neuroscience2021
Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 48 citations in OpenAlex.
- Combination forecasting of COVID-19 in Bangladesh: guiding public health policy through integrated time series, ML, and DL models.BMC infectious diseases · 2025Article
- Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department.The western journal of emergency medicine · 2024Article
- Hybrid deep learning approach to improve classification of low-volume high-dimensional data.BMC bioinformatics · 2023Article
- A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.Health and technology · 2023Article
- ABOA-CNN: auction-based optimization algorithm with convolutional neural network for pulmonary disease prediction.Neural computing & applications · 2023Article
- High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms.Scientific reports · 2022Article
- COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization.Computers in biology and medicine · 2022Review
- A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images.Computational intelligence and neuroscience · 2022Article
- Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis.Computational intelligence and neuroscience · 2022Article
- Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world's healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak's spread, and restore full functionality to the world's healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification.
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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.