ArticleExpert systems2022
COVID-19 diagnosis system by deep learning approaches.
Article in Expert systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms.BMC bioinformatics · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Diagnostic of Patients with COVID-19 Pneumonia Using Passive Medical Microwave Radiometry (MWR).Diagnostics (Basel, Switzerland) · 2023Article
- Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.Computers in biology and medicine · 2023Article
- A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms.Mobile networks and applications : MONET · 2023Article
- Machine Learning and COVID-19: Lessons from SARS-CoV-2.Advances in experimental medicine and biology · 2023Article
- Towards realistic privacy-preserving deep learning over encrypted medical data.Frontiers in cardiovascular medicine · 2023Article
- Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19.IEEE journal of biomedical and health informatics · 2022Article
- COVID-19 diagnosis system by deep learning approaches.Expert systems · 2022Article
- Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images.Computers in biology and medicine · 2022Article
- COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.Frontiers in public health · 2022Article
- Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics.Pattern recognition letters · 2021Article
- A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The novel coronavirus disease 2019 (COVID-19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID-19 patients, which are not effective. The above complex circumstances need to detect suspected COVID-19 patients based on routine techniques like chest X-Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID-19 or Non-COVID-19 patients with a full-resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID-19 patient dataset. The evaluation results are generated using a fourfold cross-validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1-score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset.
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