An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images
A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenar...
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| Published in | IEEE transactions on fuzzy systems Vol. 30; no. 8; pp. 2902 - 2914 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1063-6706 1941-0034 1941-0034 |
| DOI | 10.1109/TFUZZ.2021.3097806 |
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| Abstract | A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications. |
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| AbstractList | A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications. A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications.A global pandemic scenario is witnessed worldwide owing to the menace of the rapid outbreak of the deadly COVID-19 virus. To save mankind from this apocalyptic onslaught, it is essential to curb the fast spreading of this dreadful virus. Moreover, the absence of specialized drugs has made the scenario even more badly and thus an early-stage adoption of necessary precautionary measures would provide requisite supportive treatment for its prevention. The prime objective of this article is to use radiological images as a tool to help in early diagnosis. The interval type 2 fuzzy clustering is blended with the concept of superpixels, and metaheuristics to efficiently segment the radiological images. Despite noise sensitivity of watershed-based approach, it is adopted for superpixel computation owing to its simplicity where the noise problem is handled by the important edge information of the gradient image is preserved with the help of morphological opening and closing based reconstruction operations. The traditional objective function of the fuzzy c-means clustering algorithm is modified to incorporate the spatial information from the neighboring superpixel-based local window. The computational overhead associated with the processing of a huge amount of spatial information is reduced by incorporating the concept of superpixels and the optimal clusters are determined by a modified version of the flower pollination algorithm. Although the proposed approach performs well but should not be considered as an alternative to gold standard detection tests of COVID-19. Experimental results are found to be promising enough to deploy this approach for real-life applications. |
| Author | Ding, Weiping Chatterjee, Sankhadeep Nayak, Janmenjoy Chakraborty, Shouvik Mali, Kalyani Das, Asit Kumar Banerjee, Soumen |
| AuthorAffiliation | Department of Computer Science and Engineering Aditya Institute of Technology and Management 470169 Srikakulam 532201 India Department of Computer Science and Engineering University of Kalyani 30132 Kalyani 741235 India School of Information Science and Technology Nantong University 66479 Nantong 226019 China Department of Computer Science and Engineering University of Engineering and Management 121690 Kolkata 700160 India Department of Computer Science and Technology Indian Institute of Engineering Science and Technology 30130 Howrah 711103 India Department of Electronics and Communication Engineering University of Engineering and Management 121690 Kolkata 700160 India |
| AuthorAffiliation_xml | – name: School of Information Science and Technology Nantong University 66479 Nantong 226019 China – name: Department of Computer Science and Engineering Aditya Institute of Technology and Management 470169 Srikakulam 532201 India – name: Department of Computer Science and Engineering University of Kalyani 30132 Kalyani 741235 India – name: Department of Computer Science and Technology Indian Institute of Engineering Science and Technology 30130 Howrah 711103 India – name: Department of Computer Science and Engineering University of Engineering and Management 121690 Kolkata 700160 India – name: Department of Electronics and Communication Engineering University of Engineering and Management 121690 Kolkata 700160 India |
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| SubjectTerms | Algorithms Clustering Computed tomography Coronaviruses COVID-19 Disease transmission Fuzzy logic Fuzzy sets Heuristic methods Image segmentation interval type-2 fuzzy system Lung Noise sensitivity radiological image segmentation Spatial data unsupervised fuzzy clustering Viral diseases Viruses X-rays |
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| Title | An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 From Radiological Images |
| URI | https://ieeexplore.ieee.org/document/9490300 https://www.ncbi.nlm.nih.gov/pubmed/36345371 https://www.proquest.com/docview/2697566117 https://www.proquest.com/docview/2734165483 https://pubmed.ncbi.nlm.nih.gov/PMC9454279 https://ieeexplore.ieee.org/ielx7/91/9849079/09490300.pdf |
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