Cargando…

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learni...

Descripción completa

Detalles Bibliográficos
Autores principales: Shivahare, Basu Dev, Gupta, S. K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968355/
https://www.ncbi.nlm.nih.gov/pubmed/35368938
http://dx.doi.org/10.1155/2022/9009406
_version_ 1784679028271087616
author Shivahare, Basu Dev
Gupta, S. K.
author_facet Shivahare, Basu Dev
Gupta, S. K.
author_sort Shivahare, Basu Dev
collection PubMed
description This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person's life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1–F7), multimodal (F8–F13), and fixed-dimension multimodal (F14–F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu's maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images.
format Online
Article
Text
id pubmed-8968355
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-89683552022-04-01 Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm Shivahare, Basu Dev Gupta, S. K. J Healthc Eng Research Article This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person's life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1–F7), multimodal (F8–F13), and fixed-dimension multimodal (F14–F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu's maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images. Hindawi 2022-03-30 /pmc/articles/PMC8968355/ /pubmed/35368938 http://dx.doi.org/10.1155/2022/9009406 Text en Copyright © 2022 Basu Dev Shivahare and S. K. Gupta. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Shivahare, Basu Dev
Gupta, S. K.
Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm
title Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm
title_full Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm
title_fullStr Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm
title_full_unstemmed Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm
title_short Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm
title_sort efficient covid-19 ct scan image segmentation by automatic clustering algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8968355/
https://www.ncbi.nlm.nih.gov/pubmed/35368938
http://dx.doi.org/10.1155/2022/9009406
work_keys_str_mv AT shivaharebasudev efficientcovid19ctscanimagesegmentationbyautomaticclusteringalgorithm
AT guptask efficientcovid19ctscanimagesegmentationbyautomaticclusteringalgorithm