ArticleHeliyon2024
ROI extraction in corona virus (COVID 19) CT images using intuitionistic fuzzy edge detection.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed, 3 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Edge detection is a vital aspect of medical image processing, playing a key role in delineating borders and contours within images. This capability is instrumental for various applications, including segmentation, feature extraction, and diagnostic procedures in the realm of medical imaging. COVID-19 is a deadly disease affecting people in most of countries in the world. COVID-19 is due to the coronavirus which belongs to the family of RNA viruses and causes various symptoms such as pneumonia, fever, breathing difficulty, and lung infection. ROI extraction plays a vital role in disease diagnosis and therapeutic treatment. CT scans can help detect abnormalities in the lungs that are characteristic of COVID-19, such as ground-glass opacities and consolidation. This research work proposes an Intuitionistic fuzzy (IF) edge detector for the segmentation of COVID-19 CT images. Intuitionistic fuzzy sets go beyond conventional fuzzy sets by incorporating an additional parameter, referred to as the hesitation degree or non-membership degree. This extra parameter enhances the ability to represent uncertainty more intricately in expressing the degree to which an element may or may not belong to a set. The IF edge detector generates proficient results, when compared with the traditional edge detection algorithms and is validated in terms of performance metrics for benchmark images. Intuitionistic fuzzy edge detection has been shown to be effective in handling uncertainty and imprecision in edge detection.
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.