ArticleExpert systems with applications2021
SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images.
Article in Expert systems with applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.Biomedical signal processing and control · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- SUFEMO: A superpixel based fuzzy image segmentation method for COVID-19 radiological image elucidation.Applied soft computing · 2022Article
- X-ray image based COVID-19 detection using evolutionary deep learning approach.Expert systems with applications · 2022Article
- An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images.IEEE transactions on fuzzy systems : a publication of the IEEE Neural Networks Council · 2022Article
- Metaheuristics based COVID-19 detection using medical images: A review.Computers in biology and medicine · 2022Review
- Contour-enhanced attention CNN for CT-based COVID-19 segmentation.Pattern recognition · 2022Article
- COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation.Computers & graphics · 2022Article
- Novel deep learning approach to model and predict the spread of COVID-19.Intelligent systems with applications · 2022Article
- A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach.Applied soft computing · 2022Article
- SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation.Expert systems with applications · 2021Article
- Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals.International journal of production economics · 2021Article
- A morphology-based radiological image segmentation approach for efficient screening of COVID-19.Biomedical signal processing and control · 2021Article
- A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 or COVID-19 is one of the biggest challenges which are being faced by mankind. Researchers are continuously trying to discover a vaccine or medicine for this highly infectious disease but, proper success is not achieved to date. Many countries are suffering from this disease and trying to find some solution that can prevent the dramatic spread of this virus. Although the mortality rate is not very high, the highly infectious nature of this virus makes it a global threat. RT-PCR test is the only means to confirm the presence of this virus to date. Only precautionary measures like early screening, frequent hand wash, social distancing use of masks, and other protective equipment can prevent us from this virus. Some researches show that the radiological images can be quite helpful for the early screening purpose because some features of the radiological images indicate the presence of the COVID-19 virus and therefore, it can serve as an effective screening tool. Automated analysis of these radiological images can help the physicians and other domain experts to study and screen the suspected patients easily and reliably within the stipulated amount of time. This method may not replace the traditional RT-PCR method for detection but, it can be helpful to filter the suspected patients from the rest of the community that can effectively reduce the spread in the of this virus. A novel method is proposed in this work to segment the radiological images for the better explication of the COVID-19 radiological images. The proposed method will be known as SuFMoFPA (Superpixel based Fuzzy Modified Flower Pollination Algorithm). The type 2 fuzzy clustering system is blended with this proposed approach to get the better-segmented outcome. Obtained results are quite promising and outperforming some of the standard approaches which are encouraging for the practical uses of the proposed approach to screening the COVID-19 patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.