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Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis
Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages m...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315657/ https://www.ncbi.nlm.nih.gov/pubmed/35888172 http://dx.doi.org/10.3390/life12071084 |
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author | Almalki, Yassir Edrees Ali, Muhammad Umair Ahmed, Waqas Kallu, Karam Dad Zafar, Amad Alduraibi, Sharifa Khalid Irfan, Muhammad Basha, Mohammad Abd Alkhalik Alshamrani, Hassan A. Alduraibi, Alaa Khalid |
author_facet | Almalki, Yassir Edrees Ali, Muhammad Umair Ahmed, Waqas Kallu, Karam Dad Zafar, Amad Alduraibi, Sharifa Khalid Irfan, Muhammad Basha, Mohammad Abd Alkhalik Alshamrani, Hassan A. Alduraibi, Alaa Khalid |
author_sort | Almalki, Yassir Edrees |
collection | PubMed |
description | Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors. |
format | Online Article Text |
id | pubmed-9315657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93156572022-07-27 Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis Almalki, Yassir Edrees Ali, Muhammad Umair Ahmed, Waqas Kallu, Karam Dad Zafar, Amad Alduraibi, Sharifa Khalid Irfan, Muhammad Basha, Mohammad Abd Alkhalik Alshamrani, Hassan A. Alduraibi, Alaa Khalid Life (Basel) Article Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors. MDPI 2022-07-20 /pmc/articles/PMC9315657/ /pubmed/35888172 http://dx.doi.org/10.3390/life12071084 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Almalki, Yassir Edrees Ali, Muhammad Umair Ahmed, Waqas Kallu, Karam Dad Zafar, Amad Alduraibi, Sharifa Khalid Irfan, Muhammad Basha, Mohammad Abd Alkhalik Alshamrani, Hassan A. Alduraibi, Alaa Khalid Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis |
title | Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis |
title_full | Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis |
title_fullStr | Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis |
title_full_unstemmed | Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis |
title_short | Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis |
title_sort | robust gaussian and nonlinear hybrid invariant clustered features aided approach for speeded brain tumor diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315657/ https://www.ncbi.nlm.nih.gov/pubmed/35888172 http://dx.doi.org/10.3390/life12071084 |
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