ReviewJournal of King Saud University. Computer and information sciences2022
DeSa COVID-19: Deep salient COVID-19 image-based quality assessment.
Review in Journal of King Saud University. Computer and information sciences, 2022. 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.
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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study offers an advanced method to evaluate the coronavirus disease 2019 (COVID-19) image quality. The salient COVID-19 image map is incorporated with the deep convolutional neural network (DCNN), namely DeSa COVID-19, which exerts the n-convex method for the full-reference image quality assessment (FR-IQA). The glaring outcomes substantiate that DeSa COVID-19 and the recommended DCNN architecture can convey a remarkable accomplishment on the COVID-chestxray and the COVID-CT datasets, respectively. The salient COVID-19 image map is also gauged in the minuscule COVID-19 image patches. The exploratory results attest that DeSa COVID-19 and the recommended DCNN methods are very good accomplishment compared with other advanced methods on COVID-chestxray and COVID-CT datasets, respectively. The recommended DCNN also acquires the enhanced outgrowths faced with several advanced full-reference-medical-image-quality-assessment (FR-MIQA) techniques in the fast fading (FF), blocking artifact (BA), white noise Gaussian (WG), JPEG, and JPEG2000 (JP2K) in the distorted and undistorted COVID-19 images. The Spearman's rank order correlation coefficient (SROCC) and the linear correlation coefficient (LCC) appraise the recommended DCNN and DeSa COVID-19 fulfillment which are compared the recent FR-MIQA methods. The DeSa COVID-19 evaluation outshines
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.