An automated and risk free WHO grading of glioma from MRI images using CNN
Abstract Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are clinically used to determine the grades of glioma. Depending upon the findings of these methods, treatment is planned to improve the life exp...
Ausführliche Beschreibung
Autor*in: |
Gilanie, Ghulam [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2022), 2 vom: 12. Juli, Seite 2857-2869 |
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Übergeordnetes Werk: |
volume:82 ; year:2022 ; number:2 ; day:12 ; month:07 ; pages:2857-2869 |
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DOI / URN: |
10.1007/s11042-022-13415-9 |
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OLC2080217275 |
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10.1007/s11042-022-13415-9 doi (DE-627)OLC2080217275 (DE-He213)s11042-022-13415-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Gilanie, Ghulam verfasserin aut An automated and risk free WHO grading of glioma from MRI images using CNN 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are clinically used to determine the grades of glioma. Depending upon the findings of these methods, treatment is planned to improve the life expectancy of the controls. Magnetic resonance imaging (MRI), the most widely used medical imaging modality to diagnose a brain tumor, is producing a huge volume of MRI data. A reliable, automatic, and noninvasive method of glioma grading are always required as an alternative to these invasive methods. In this research, a model has been proposed using Convolutional Neural Networks to classify low and high-grade glioma. A locally organized dataset, developed in the Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan has been used for research and experiments. Additionally, results have also been validated on a publicly available benchmarked dataset, i.e., BraTS-2017. The proposed method demonstrated significant achievement in terms of classification rates, i.e., the accuracy of 98.93% (for low-grade glioma) and 98.12% (for high-grade glioma). Experimental results proved that the proposed model is accurate (98.52%) and is efficient in glioma grade identification. WHO-Grading Glioma grading (low/high) CNN for glioma grading Bajwa, Usama Ijaz (orcid)0000-0001-5755-1194 aut Waraich, Mustansar Mahmood aut Anwar, Muhammad Waqas aut Ullah, Hafeez aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 2 vom: 12. Juli, Seite 2857-2869 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:2 day:12 month:07 pages:2857-2869 https://doi.org/10.1007/s11042-022-13415-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 2 12 07 2857-2869 |
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10.1007/s11042-022-13415-9 doi (DE-627)OLC2080217275 (DE-He213)s11042-022-13415-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Gilanie, Ghulam verfasserin aut An automated and risk free WHO grading of glioma from MRI images using CNN 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are clinically used to determine the grades of glioma. Depending upon the findings of these methods, treatment is planned to improve the life expectancy of the controls. Magnetic resonance imaging (MRI), the most widely used medical imaging modality to diagnose a brain tumor, is producing a huge volume of MRI data. A reliable, automatic, and noninvasive method of glioma grading are always required as an alternative to these invasive methods. In this research, a model has been proposed using Convolutional Neural Networks to classify low and high-grade glioma. A locally organized dataset, developed in the Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan has been used for research and experiments. Additionally, results have also been validated on a publicly available benchmarked dataset, i.e., BraTS-2017. The proposed method demonstrated significant achievement in terms of classification rates, i.e., the accuracy of 98.93% (for low-grade glioma) and 98.12% (for high-grade glioma). Experimental results proved that the proposed model is accurate (98.52%) and is efficient in glioma grade identification. WHO-Grading Glioma grading (low/high) CNN for glioma grading Bajwa, Usama Ijaz (orcid)0000-0001-5755-1194 aut Waraich, Mustansar Mahmood aut Anwar, Muhammad Waqas aut Ullah, Hafeez aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 2 vom: 12. Juli, Seite 2857-2869 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:2 day:12 month:07 pages:2857-2869 https://doi.org/10.1007/s11042-022-13415-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 2 12 07 2857-2869 |
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10.1007/s11042-022-13415-9 doi (DE-627)OLC2080217275 (DE-He213)s11042-022-13415-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Gilanie, Ghulam verfasserin aut An automated and risk free WHO grading of glioma from MRI images using CNN 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are clinically used to determine the grades of glioma. Depending upon the findings of these methods, treatment is planned to improve the life expectancy of the controls. Magnetic resonance imaging (MRI), the most widely used medical imaging modality to diagnose a brain tumor, is producing a huge volume of MRI data. A reliable, automatic, and noninvasive method of glioma grading are always required as an alternative to these invasive methods. In this research, a model has been proposed using Convolutional Neural Networks to classify low and high-grade glioma. A locally organized dataset, developed in the Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan has been used for research and experiments. Additionally, results have also been validated on a publicly available benchmarked dataset, i.e., BraTS-2017. The proposed method demonstrated significant achievement in terms of classification rates, i.e., the accuracy of 98.93% (for low-grade glioma) and 98.12% (for high-grade glioma). Experimental results proved that the proposed model is accurate (98.52%) and is efficient in glioma grade identification. WHO-Grading Glioma grading (low/high) CNN for glioma grading Bajwa, Usama Ijaz (orcid)0000-0001-5755-1194 aut Waraich, Mustansar Mahmood aut Anwar, Muhammad Waqas aut Ullah, Hafeez aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 2 vom: 12. Juli, Seite 2857-2869 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:2 day:12 month:07 pages:2857-2869 https://doi.org/10.1007/s11042-022-13415-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 2 12 07 2857-2869 |
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10.1007/s11042-022-13415-9 doi (DE-627)OLC2080217275 (DE-He213)s11042-022-13415-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Gilanie, Ghulam verfasserin aut An automated and risk free WHO grading of glioma from MRI images using CNN 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are clinically used to determine the grades of glioma. Depending upon the findings of these methods, treatment is planned to improve the life expectancy of the controls. Magnetic resonance imaging (MRI), the most widely used medical imaging modality to diagnose a brain tumor, is producing a huge volume of MRI data. A reliable, automatic, and noninvasive method of glioma grading are always required as an alternative to these invasive methods. In this research, a model has been proposed using Convolutional Neural Networks to classify low and high-grade glioma. A locally organized dataset, developed in the Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan has been used for research and experiments. Additionally, results have also been validated on a publicly available benchmarked dataset, i.e., BraTS-2017. The proposed method demonstrated significant achievement in terms of classification rates, i.e., the accuracy of 98.93% (for low-grade glioma) and 98.12% (for high-grade glioma). Experimental results proved that the proposed model is accurate (98.52%) and is efficient in glioma grade identification. WHO-Grading Glioma grading (low/high) CNN for glioma grading Bajwa, Usama Ijaz (orcid)0000-0001-5755-1194 aut Waraich, Mustansar Mahmood aut Anwar, Muhammad Waqas aut Ullah, Hafeez aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 2 vom: 12. Juli, Seite 2857-2869 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:2 day:12 month:07 pages:2857-2869 https://doi.org/10.1007/s11042-022-13415-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 2 12 07 2857-2869 |
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title_sort |
an automated and risk free who grading of glioma from mri images using cnn |
title_auth |
An automated and risk free WHO grading of glioma from MRI images using CNN |
abstract |
Abstract Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are clinically used to determine the grades of glioma. Depending upon the findings of these methods, treatment is planned to improve the life expectancy of the controls. Magnetic resonance imaging (MRI), the most widely used medical imaging modality to diagnose a brain tumor, is producing a huge volume of MRI data. A reliable, automatic, and noninvasive method of glioma grading are always required as an alternative to these invasive methods. In this research, a model has been proposed using Convolutional Neural Networks to classify low and high-grade glioma. A locally organized dataset, developed in the Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan has been used for research and experiments. Additionally, results have also been validated on a publicly available benchmarked dataset, i.e., BraTS-2017. The proposed method demonstrated significant achievement in terms of classification rates, i.e., the accuracy of 98.93% (for low-grade glioma) and 98.12% (for high-grade glioma). Experimental results proved that the proposed model is accurate (98.52%) and is efficient in glioma grade identification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
abstractGer |
Abstract Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are clinically used to determine the grades of glioma. Depending upon the findings of these methods, treatment is planned to improve the life expectancy of the controls. Magnetic resonance imaging (MRI), the most widely used medical imaging modality to diagnose a brain tumor, is producing a huge volume of MRI data. A reliable, automatic, and noninvasive method of glioma grading are always required as an alternative to these invasive methods. In this research, a model has been proposed using Convolutional Neural Networks to classify low and high-grade glioma. A locally organized dataset, developed in the Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan has been used for research and experiments. Additionally, results have also been validated on a publicly available benchmarked dataset, i.e., BraTS-2017. The proposed method demonstrated significant achievement in terms of classification rates, i.e., the accuracy of 98.93% (for low-grade glioma) and 98.12% (for high-grade glioma). Experimental results proved that the proposed model is accurate (98.52%) and is efficient in glioma grade identification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
abstract_unstemmed |
Abstract Glioma is among aggressive and common brain tumors, with a low survival rate, in its highest grade. Invasive methods, i.e., biopsy and spinal tap are clinically used to determine the grades of glioma. Depending upon the findings of these methods, treatment is planned to improve the life expectancy of the controls. Magnetic resonance imaging (MRI), the most widely used medical imaging modality to diagnose a brain tumor, is producing a huge volume of MRI data. A reliable, automatic, and noninvasive method of glioma grading are always required as an alternative to these invasive methods. In this research, a model has been proposed using Convolutional Neural Networks to classify low and high-grade glioma. A locally organized dataset, developed in the Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan has been used for research and experiments. Additionally, results have also been validated on a publicly available benchmarked dataset, i.e., BraTS-2017. The proposed method demonstrated significant achievement in terms of classification rates, i.e., the accuracy of 98.93% (for low-grade glioma) and 98.12% (for high-grade glioma). Experimental results proved that the proposed model is accurate (98.52%) and is efficient in glioma grade identification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
collection_details |
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container_issue |
2 |
title_short |
An automated and risk free WHO grading of glioma from MRI images using CNN |
url |
https://doi.org/10.1007/s11042-022-13415-9 |
remote_bool |
false |
author2 |
Bajwa, Usama Ijaz Waraich, Mustansar Mahmood Anwar, Muhammad Waqas Ullah, Hafeez |
author2Str |
Bajwa, Usama Ijaz Waraich, Mustansar Mahmood Anwar, Muhammad Waqas Ullah, Hafeez |
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hochschulschrift_bool |
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doi_str |
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up_date |
2024-07-04T03:14:30.197Z |
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