ArticlePloS one2022
Automated detection of COVID-19 through convolutional neural network using chest x-ray images.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- Quantum classical hybrid convolutional neural networks for breast cancer diagnosis.Scientific reports · 2024Article
- Contribution to pulmonary diseases diagnostic from X-ray images using innovative deep learning models.Heliyon · 2024Article
- A brief review and scientometric analysis on ensemble learning methods for handling COVID-19.Heliyon · 2024Article
- CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes.Diagnostics (Basel, Switzerland) · 2024Article
- Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests.Scientific reports · 2023Article
- A Deep Learning Model Based on Capsule Networks for COVID Diagnostics through X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia.Diagnostics (Basel, Switzerland) · 2023Article
- A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.Health and technology · 2023Article
- On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories.Healthcare (Basel, Switzerland) · 2023Article
- Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation.Journal of imaging · 2023Article
- The development of new remote technologies in disaster medicine education: A scoping review.Frontiers in public health · 2023Article
- Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.New generation computing · 2023Article
- Generative adversarial network based data augmentation for CNN based detection of Covid-19.Scientific reports · 2022Article
- Review
- Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Chest X-Ray Images to Differentiate COVID-19 from Pneumonia with Artificial Intelligence Techniques.International journal of biomedical imaging · 2022Article
- COVID-19 Data Analytics Using Extended Convolutional Technique.Interdisciplinary perspectives on infectious diseases · 2022Article
- Deep learning techniques for detecting and recognizing face masks: A survey.Frontiers in public health · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.
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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.