ArticleComputers in biology and medicine2022
Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 98 citations in OpenAlex.
- Metaheuristic-Optimized Convolutional Neural Network for Automated Diagnosis of Viral Pneumonia and Tuberculosis from Chest X-Rays.Diagnostics (Basel, Switzerland) · 2026Article
- Analysis of hybrid CNN models optimized with metaheuristic algorithms for melanoma detection.Scientific reports · 2026Article
- Transfer learning with Bayesian optimization for colorectal cancer histopathology classification.BMC medical imaging · 2026Article
- Machine-Learning-Enabled Diagnostics with Improved Visualization of Disease Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- The impact of Bayesian optimization on feature selection.Scientific reports · 2024Article
- Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.Biocybernetics and biomedical engineering · 2023Article
- A survey of machine learning-based methods for COVID-19 medical image analysis.Medical & biological engineering & computing · 2023Review
- Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images.Healthcare (Basel, Switzerland) · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation.Journal of imaging · 2023Article
- LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases.Sensors (Basel, Switzerland) · 2023Article
- Quantitative and Qualitative Analysis of 18 Deep Convolutional Neural Network (CNN) Models with Transfer Learning to Diagnose COVID-19 on Chest X-Ray (CXR) Images.SN computer science · 2023Article
- 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
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.Quantitative imaging in medicine and surgery · 2023Article
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
- Multi-texture features and optimized DeepNet for COVID-19 detection using chest x-ray images.Concurrency and computation : practice & experience · 2022Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains and emerged as a key technology in Artificial Intelligence. Recent advances in visual recognition are based on image classification and artefacts detection within these images. The purpose of this study is to classify chest X-ray images of COVID-19 artefacts in changed real-world situations. A novel Bayesian optimization-based convolutional neural network (CNN) model is proposed for the recognition of chest X-ray images. The proposed model has two main components. The first one utilizes CNN to extract and learn deep features. The second component is a Bayesian-based optimizer that is used to tune the CNN hyperparameters according to an objective function. The used large-scale and balanced dataset comprises 10,848 images (i.e., 3616 COVID-19, 3616 normal cases, and 3616 Pneumonia). In the first ablation investigation, we compared Bayesian optimization to three distinct ablation scenarios. We used convergence charts and accuracy to compare the three scenarios. We noticed that the Bayesian search-derived optimal architecture achieved 96% accuracy. To assist qualitative researchers, address their research questions in a methodologically sound manner, a comparison of research method and theme analysis methods was provided. The suggested model is shown to be more trustworthy and accurate in real world.
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