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Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion
A neurological disorder is a problem with the neural system of the body, as a brain tumor is one of the deadliest neurological conditions and it requires an early and effective detection procedure. The existing detection and diagnosis methods for image evaluation are based on the judgment of the rad...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356811/ https://www.ncbi.nlm.nih.gov/pubmed/35942449 http://dx.doi.org/10.1155/2022/2609387 |
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author | Kshirsagar, Pravin R. Manoharan, Hariprasath Siva Nagaraju, V Alqahtani, Hamed Noorulhasan, Quadri Islam, Saiful Thangamani, M. Sahni, Varsha Adigo, Amsalu Gosu |
author_facet | Kshirsagar, Pravin R. Manoharan, Hariprasath Siva Nagaraju, V Alqahtani, Hamed Noorulhasan, Quadri Islam, Saiful Thangamani, M. Sahni, Varsha Adigo, Amsalu Gosu |
author_sort | Kshirsagar, Pravin R. |
collection | PubMed |
description | A neurological disorder is a problem with the neural system of the body, as a brain tumor is one of the deadliest neurological conditions and it requires an early and effective detection procedure. The existing detection and diagnosis methods for image evaluation are based on the judgment of the radiologist and neurospecialist, where a risk of human mistakes can be found. Therefore, a new flanged method and methodology for detecting brain tumors using magnetic resonance imaging and the artificial neural network (ANN) technique are applied. The research is based on an artificial neural network-based behavioral examination of neurological disorders. In this study, an artificial neural network is used to detect a brain tumor as early as possible. The current work develops an effective approach for detecting cancer from a given brain MRI and recognizing the retrieved data for further use. To obtain the desired result, the following three procedures are used: preprocessing, feature extraction, training, and detection or classification. A Gaussian filter is also incorporated to eliminate noise from the image, and for texture feature extraction, GLCM is considered in this study. Further entropy, contrast, energy, homogeneity, and other GLCM texture properties of tumor categorization are measured using the ANFIS approach, which determines if the tumor is normal, benign, or malignant. Future research will focus on applying advanced texture analysis to classify brain tumors into distinct classes in order to improve the accuracy of brain tumor diagnosis. In the future, MRI brain imaging will be used to classify metastatic brain tumors. |
format | Online Article Text |
id | pubmed-9356811 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-93568112022-08-07 Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion Kshirsagar, Pravin R. Manoharan, Hariprasath Siva Nagaraju, V Alqahtani, Hamed Noorulhasan, Quadri Islam, Saiful Thangamani, M. Sahni, Varsha Adigo, Amsalu Gosu Comput Intell Neurosci Research Article A neurological disorder is a problem with the neural system of the body, as a brain tumor is one of the deadliest neurological conditions and it requires an early and effective detection procedure. The existing detection and diagnosis methods for image evaluation are based on the judgment of the radiologist and neurospecialist, where a risk of human mistakes can be found. Therefore, a new flanged method and methodology for detecting brain tumors using magnetic resonance imaging and the artificial neural network (ANN) technique are applied. The research is based on an artificial neural network-based behavioral examination of neurological disorders. In this study, an artificial neural network is used to detect a brain tumor as early as possible. The current work develops an effective approach for detecting cancer from a given brain MRI and recognizing the retrieved data for further use. To obtain the desired result, the following three procedures are used: preprocessing, feature extraction, training, and detection or classification. A Gaussian filter is also incorporated to eliminate noise from the image, and for texture feature extraction, GLCM is considered in this study. Further entropy, contrast, energy, homogeneity, and other GLCM texture properties of tumor categorization are measured using the ANFIS approach, which determines if the tumor is normal, benign, or malignant. Future research will focus on applying advanced texture analysis to classify brain tumors into distinct classes in order to improve the accuracy of brain tumor diagnosis. In the future, MRI brain imaging will be used to classify metastatic brain tumors. Hindawi 2022-07-30 /pmc/articles/PMC9356811/ /pubmed/35942449 http://dx.doi.org/10.1155/2022/2609387 Text en Copyright © 2022 Pravin R. Kshirsagar et al. 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 Kshirsagar, Pravin R. Manoharan, Hariprasath Siva Nagaraju, V Alqahtani, Hamed Noorulhasan, Quadri Islam, Saiful Thangamani, M. Sahni, Varsha Adigo, Amsalu Gosu Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion |
title | Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion |
title_full | Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion |
title_fullStr | Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion |
title_full_unstemmed | Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion |
title_short | Accrual and Dismemberment of Brain Tumours Using Fuzzy Interface and Grey Textures for Image Disproportion |
title_sort | accrual and dismemberment of brain tumours using fuzzy interface and grey textures for image disproportion |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9356811/ https://www.ncbi.nlm.nih.gov/pubmed/35942449 http://dx.doi.org/10.1155/2022/2609387 |
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