ArticleKnowledge-based systems2021
Ambiguous D-means fusion clustering algorithm based on ambiguous set theory: Special application in clustering of CT scan images of COVID-19.
Article in Knowledge-based systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Multimodality medical image fusion using directional total variation based linear spectral clustering in NSCT domain.Scientific reports · 2026Article
- A cutset-type kernel possibilistic fuzzy c-means method for robust image segmentation.Frontiers in artificial intelligence · 2026Article
- Comparing Different Contrast Injection Methods for Multislice Spiral CT Imaging in Triple-Rule-Out Examinations: A Study on Acute Chest Pain Patients.International journal of general medicine · 2025Article
- Continuum topological derivative - A novel application tool for segmentation of CT and MRI images.Neuroimage. Reports · 2024Article
- Remote sensing image information extraction based on Compensated Fuzzy Neural Network and big data analytics.BMC medical imaging · 2024Article
- A Pig Mass Estimation Model Based on Deep Learning without Constraint.Animals : an open access journal from MDPI · 2023Article
- Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution algorithm.Neural computing & applications · 2023Article
- Diagnosis of cardiac abnormalities based on phonocardiogram using a novel fuzzy matching feature extraction method.BMC medical informatics and decision making · 2022Article
- Distributed lag interrupted time series model for unclear intervention timing: effect of a statement of emergency during COVID-19 pandemic.BMC medical research methodology · 2022Article
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2 authors.
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Abstract
Coronavirus Disease 2019 (COVID-19) has been considered one of the most critical diseases of the 21st century. Only early detection can aid in the prevention of personal transmission of the disease. Recent scientific research reports indicate that computed tomography (CT) images of COVID-19 patients exhibit acute infections and lung abnormalities. However, analyzing these CT scan images is very difficult because of the presence of noise and low-resolution. Therefore, this study suggests the development of a new early detection method to detect abnormalities in chest CT scan images of COVID-19 patients. By this motivation, a novel image clustering algorithm, called
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