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Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images
Dey,Nilanjan1; Rajinikanth,V.2; Fong,Simon James3,4; Kaiser,M. Shamim5; Mahmud,Mufti6
2020-09-01
Source PublicationCognitive Computation
ISSN1866-9956
Volume12Issue:5Pages:1011-1023
AbstractThe 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.
KeywordCOVID-19 infection CT scan image Fused feature vector KNN classifier Segmentation and detection accuracy
DOI10.1007/s12559-020-09751-3
URLView the original
Language英语
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Cited Times [WOS]:16   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorDey,Nilanjan
Affiliation1.Department of Information Technology,Techno India College of Technology,Kolkata,700156,India
2.Department of Electronics and Instrumentation Engineering,St. Joseph’s College of Engineering,Chennai,600119,India
3.Department of Computer and Information Science,University of Macau,Taipa,China
4.DACC Laboratory,Zhuhai Institutes of Advanced Technology of the Chinese Academy of Sciences,Zhuhai,China
5.Institute of Information Technology,Jahangirnagar University,Savar,1342,Bangladesh
6.Department of Computing & Technology,Nottingham Trent University,Clifton Lane,NG11 8NS,United Kingdom
Recommended Citation
GB/T 7714
Dey,Nilanjan,Rajinikanth,V.,Fong,Simon James,et al. Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images[J]. Cognitive Computation,2020,12(5):1011-1023.
APA Dey,Nilanjan,Rajinikanth,V.,Fong,Simon James,Kaiser,M. Shamim,&Mahmud,Mufti.(2020).Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images.Cognitive Computation,12(5),1011-1023.
MLA Dey,Nilanjan,et al."Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images".Cognitive Computation 12.5(2020):1011-1023.
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