ArticleFrontiers in genetics2022
Detecting COVID-19 infection status from chest X-ray and CT scan
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 33 citations in OpenAlex.
- A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks.Scientific reports · 2026Article
- Viral pneumonia detection during the COVID-19 pandemic using deep learning and DCGAN-based data augmentation.Scientific reports · 2026Article
- HyperHealth: a pilot study on AI-driven COVID-19 detection using hyperspectral fingertip images.Scientific reports · 2026Article
- Spatial Attention-guided Hybrid Deep Learning with Sharpened Cosine Similarity for Accurate Chest X-ray InterpretationCurrent medical imaging · 2026Article
- An explainable machine learning framework for cardiovascular risk prediction using structured health data.Frontiers in artificial intelligence · 2026Article
- CardiaTics: An explainable AI integrated heart disease diagnosis model with feature engineering and stacked ensemble approach.Journal of big data · 2026Article
- An efficient deep learning-based morphology aware hierarchical mixture of features for tuberculosis screening using segmentation of chest X-ray images.Scientific reports · 2025Article
- Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images.Bioengineering (Basel, Switzerland) · 2024Article
- Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images.iScience · 2024Article
- Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.Viruses · 2023Article
- A diagnosis model for brain atrophy using deep learning and MRI of type 2 diabetes mellitus.Frontiers in neuroscience · 2023Article
- 3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection.Frontiers in genetics · 2023Article
- Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture.Intelligent systems with applications · 2022Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.
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