About: Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

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About: Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images
Creator	Dey, Nilanjan Simon, · Rajinikanth, · Fong, James Mufti, · Shamim Kaiser, ·
Source	Medline; PMC
abstract	The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur’s entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.
has issue date	2020-08-15(xsd:dateTime)
bibo:doi	10.1007/s12559-020-09751-3
bibo:pmid	32837591
has license	cc-by
sha1sum (hex)	455eb34fd31727dc2a13732a927ff0d206c31dd5
schema:url	https://doi.org/10.1007/s12559-020-09751-3
resource representing a document's title	Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images
has PubMed Central identifier	PMC7429098
has PubMed identifier	32837591
schema:publication	Cognit Comput
resource representing a document's body	covid:455eb34fd31727dc2a13732a927ff0d206c31dd5#body_text
is schema:about of	named entity 'Kapur' named entity 'datasets' named entity 'infection' named entity 'Basis' named entity 'NOVEL CORONAVIRUS' named entity 'SEGMENTATION' named entity 'RANDOM FOREST' named entity 'MACHINE LEARNING' named entity 'TEST' named entity 'NOTED' named entity 'ENTROPY' named entity 'INFECTION' named entity 'CTI' named entity 'VECTOR' covid:arg/455eb34fd31727dc2a13732a927ff0d206c31dd5 named entity 'COVID-19' named entity 'infection' named entity 'COVID-19' named entity 'CTI' named entity 'classifying' named entity 'segmentation' named entity 'CTI' named entity 'aims' named entity 'fusing' named entity 'classifiers' named entity 'thresholding' named entity 'infection' named entity 'features' named entity 'support' named entity 'Random Forest' named entity 'Decision Tree' named entity 'compared' named entity 'ongoing' named entity 'classifiers' named entity 'Group' named entity 'Kapur' named entity 'infection' named entity 'COVID-19' named entity 'morphology' named entity 'classifiers' named entity 'morphology' named entity 'classifiers' named entity 'COVID-19 infection' named entity 'coronavirus disease' named entity 'lung' named entity 'Computed Tomography Images' named entity 'Infection' named entity 'Social Group' named entity 'Segmentation' named entity 'pneumonia' named entity 'KMC' named entity '1, 2' named entity 'lung' named entity 'medical image analysis' named entity 'infection' named entity 'CTI' named entity 'COVID-19' named entity 'Creative Commons Attribution 4.0' named entity 'lung' named entity 'Support Vector Machine' named entity 'feature vector' named entity 'standard deviation' named entity 'mathematical expression' named entity 'COVID' named entity 'lung' named entity 'KNN' named entity 'morphological' named entity 'algorithm' named entity 'binary form' named entity 'morbidity rates' named entity 'feature extraction' named entity 'COVID-19' named entity 'morphology' named entity 'true negative' named entity 'COVID-19' named entity 'CTI'

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