ArticleApplied soft computing2021
Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.Journal of translational medicine · 2024Article
- RETRACTED ARTICLE: COVID-19 Detection using adopted convolutional neural networks and high-performance computing.Multimedia tools and applications · 2024Article
- A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection.PloS one · 2024Article
- COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.Journal of digital imaging · 2023Article
- HDSNE a new unsupervised multiple image database fusion learning algorithm with flexible and crispy production of one database: a proof case study of lung infection diagnose In chest X-ray images.BMC medical imaging · 2023Article
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- Densely attention mechanism based network for COVID-19 detection in chest X-rays.Scientific reports · 2023Article
- Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture.SN computer science · 2023Review
- Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023Article
- Article
- RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.Expert systems with applications · 2022Article
- Study on transfer learning capabilities for pneumonia classification in chest-x-rays images.Computer methods and programs in biomedicine · 2022Article
- COVID-19 prognosis using limited chest X-ray images.Applied soft computing · 2022Article
- Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review.Archives of computational methods in engineering : state of the art reviews · 2022Review
- A Robust Framework for Epidemic Analysis, Prediction and Detection of COVID-19.Frontiers in public health · 2022Article
- Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer.Computational intelligence and neuroscience · 2021Article
- DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image.SN computer science · 2021Article
- Study on the Grading Model of Hepatic Steatosis Based on Improved DenseNet.Journal of healthcare engineeringArticle
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4).
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