ArticlePloS one2021
Automatic clustering method to segment COVID-19 CT images.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Review
- Combined Gaussian Mixture Model and Pathfinder Algorithm for Data Clustering.Entropy (Basel, Switzerland) · 2023Article
- AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19.Evolving systems · 2023Article
- A novel framework based on deep learning for COVID-19 diagnosis from X-ray images.PeerJ. Computer science · 2023Article
- Research on multi-modal autonomous diagnosis algorithm of COVID-19 based on whale optimized support vector machine and improved D-S evidence fusion.Computers in biology and medicine · 2022Article
- Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.Computational intelligence and neuroscience · 2022Article
- Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm.Journal of healthcare engineering · 2022Article
- Epidemiological Mucormycosis treatment and diagnosis challenges using the adaptive properties of computer vision techniques based approach: a review.Multimedia tools and applications · 2022Article
- 5G Use in Healthcare: The Future is Present.JSLS : Journal of the Society of Laparoendoscopic SurgeonsArticle
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Authors and funding
6 authors.
Funding
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Abstract
Coronavirus pandemic (COVID-19) has infected more than ten million persons worldwide. Therefore, researchers are trying to address various aspects that may help in diagnosis this pneumonia. Image segmentation is a necessary pr-processing step that implemented in image analysis and classification applications. Therefore, in this study, our goal is to present an efficient image segmentation method for COVID-19 Computed Tomography (CT) images. The proposed image segmentation method depends on improving the density peaks clustering (DPC) using generalized extreme value (GEV) distribution. The DPC is faster than other clustering methods, and it provides more stable results. However, it is difficult to determine the optimal number of clustering centers automatically without visualization. So, GEV is used to determine the suitable threshold value to find the optimal number of clustering centers that lead to improving the segmentation process. The proposed model is applied for a set of twelve COVID-19 CT images. Also, it was compared with traditional k-means and DPC algorithms, and it has better performance using several measures, such as PSNR, SSIM, and Entropy.
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