Risk-free WHO grading of astrocytoma using convolutional neural networks from MRI images
Abstract Astrocytoma is the most common and aggressive brain tumor, in its highest grade, the prognosis is ‘low survival rate’. Spinal tap and biopsy are the methods executed in order to determine the grade of astrocytoma. Once the grade of astrocytoma is determined, treatment is planned to improve...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gilanie, Ghulam [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 80(2020), 3 vom: 28. Sept., Seite 4295-4306 |
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| Übergeordnetes Werk: |
volume:80 ; year:2020 ; number:3 ; day:28 ; month:09 ; pages:4295-4306 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-020-09970-8 |
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OLC2122787228 |
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| 520 | |a Abstract Astrocytoma is the most common and aggressive brain tumor, in its highest grade, the prognosis is ‘low survival rate’. Spinal tap and biopsy are the methods executed in order to determine the grade of astrocytoma. Once the grade of astrocytoma is determined, treatment is planned to improve the life expectancy of oncological subjects. Spinal tap and biopsy are invasive diagnostic procedures. Magnetic resonance imaging (MRI) being widely used imaging modality to detect brain tumors, produces the large volume of MRI data each moment in clinical environments. Automated and reliable methods of astrocytoma grading from the analysis of MRI images are required as an alternative to biopsy and spinal tape. However, obtaining molecular information of brain cells using non-invasive methods is challenging. In this research work, an automatic method of astrocytoma grading using Convolutional Neural Networks (CNN) has been proposed. Results have been validated on a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan. The proposed method proved a significant achievement in terms of accuracy as 99.06% (for astrocytoma of Grade-I), 94.01% (for astrocytoma of Grade-II), 95.31% (for astrocytoma of Grade-III), 97.85% (for astrocytoma of Grade-IV), and overall accuracy of 96.56%. | ||
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10.1007/s11042-020-09970-8 doi (DE-627)OLC2122787228 (DE-He213)s11042-020-09970-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Gilanie, Ghulam verfasserin aut Risk-free WHO grading of astrocytoma using convolutional neural networks from MRI images 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Astrocytoma is the most common and aggressive brain tumor, in its highest grade, the prognosis is ‘low survival rate’. Spinal tap and biopsy are the methods executed in order to determine the grade of astrocytoma. Once the grade of astrocytoma is determined, treatment is planned to improve the life expectancy of oncological subjects. Spinal tap and biopsy are invasive diagnostic procedures. Magnetic resonance imaging (MRI) being widely used imaging modality to detect brain tumors, produces the large volume of MRI data each moment in clinical environments. Automated and reliable methods of astrocytoma grading from the analysis of MRI images are required as an alternative to biopsy and spinal tape. However, obtaining molecular information of brain cells using non-invasive methods is challenging. In this research work, an automatic method of astrocytoma grading using Convolutional Neural Networks (CNN) has been proposed. Results have been validated on a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan. The proposed method proved a significant achievement in terms of accuracy as 99.06% (for astrocytoma of Grade-I), 94.01% (for astrocytoma of Grade-II), 95.31% (for astrocytoma of Grade-III), 97.85% (for astrocytoma of Grade-IV), and overall accuracy of 96.56%. WHO grading Astrocytoma grading Convolutional neural networks Bajwa, Usama Ijaz (orcid)0000-0001-5755-1194 aut Waraich, Mustansar Mahmood aut Anwar, Muhammad Waqas aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 3 vom: 28. Sept., Seite 4295-4306 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:3 day:28 month:09 pages:4295-4306 https://doi.org/10.1007/s11042-020-09970-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 3 28 09 4295-4306 |
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10.1007/s11042-020-09970-8 doi (DE-627)OLC2122787228 (DE-He213)s11042-020-09970-8-p DE-627 ger DE-627 rakwb eng 070 004 VZ Gilanie, Ghulam verfasserin aut Risk-free WHO grading of astrocytoma using convolutional neural networks from MRI images 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Astrocytoma is the most common and aggressive brain tumor, in its highest grade, the prognosis is ‘low survival rate’. Spinal tap and biopsy are the methods executed in order to determine the grade of astrocytoma. Once the grade of astrocytoma is determined, treatment is planned to improve the life expectancy of oncological subjects. Spinal tap and biopsy are invasive diagnostic procedures. Magnetic resonance imaging (MRI) being widely used imaging modality to detect brain tumors, produces the large volume of MRI data each moment in clinical environments. Automated and reliable methods of astrocytoma grading from the analysis of MRI images are required as an alternative to biopsy and spinal tape. However, obtaining molecular information of brain cells using non-invasive methods is challenging. In this research work, an automatic method of astrocytoma grading using Convolutional Neural Networks (CNN) has been proposed. Results have been validated on a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan. The proposed method proved a significant achievement in terms of accuracy as 99.06% (for astrocytoma of Grade-I), 94.01% (for astrocytoma of Grade-II), 95.31% (for astrocytoma of Grade-III), 97.85% (for astrocytoma of Grade-IV), and overall accuracy of 96.56%. WHO grading Astrocytoma grading Convolutional neural networks Bajwa, Usama Ijaz (orcid)0000-0001-5755-1194 aut Waraich, Mustansar Mahmood aut Anwar, Muhammad Waqas aut Enthalten in Multimedia tools and applications Springer US, 1995 80(2020), 3 vom: 28. Sept., Seite 4295-4306 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:80 year:2020 number:3 day:28 month:09 pages:4295-4306 https://doi.org/10.1007/s11042-020-09970-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 80 2020 3 28 09 4295-4306 |
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risk-free who grading of astrocytoma using convolutional neural networks from mri images |
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Risk-free WHO grading of astrocytoma using convolutional neural networks from MRI images |
| abstract |
Abstract Astrocytoma is the most common and aggressive brain tumor, in its highest grade, the prognosis is ‘low survival rate’. Spinal tap and biopsy are the methods executed in order to determine the grade of astrocytoma. Once the grade of astrocytoma is determined, treatment is planned to improve the life expectancy of oncological subjects. Spinal tap and biopsy are invasive diagnostic procedures. Magnetic resonance imaging (MRI) being widely used imaging modality to detect brain tumors, produces the large volume of MRI data each moment in clinical environments. Automated and reliable methods of astrocytoma grading from the analysis of MRI images are required as an alternative to biopsy and spinal tape. However, obtaining molecular information of brain cells using non-invasive methods is challenging. In this research work, an automatic method of astrocytoma grading using Convolutional Neural Networks (CNN) has been proposed. Results have been validated on a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan. The proposed method proved a significant achievement in terms of accuracy as 99.06% (for astrocytoma of Grade-I), 94.01% (for astrocytoma of Grade-II), 95.31% (for astrocytoma of Grade-III), 97.85% (for astrocytoma of Grade-IV), and overall accuracy of 96.56%. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstractGer |
Abstract Astrocytoma is the most common and aggressive brain tumor, in its highest grade, the prognosis is ‘low survival rate’. Spinal tap and biopsy are the methods executed in order to determine the grade of astrocytoma. Once the grade of astrocytoma is determined, treatment is planned to improve the life expectancy of oncological subjects. Spinal tap and biopsy are invasive diagnostic procedures. Magnetic resonance imaging (MRI) being widely used imaging modality to detect brain tumors, produces the large volume of MRI data each moment in clinical environments. Automated and reliable methods of astrocytoma grading from the analysis of MRI images are required as an alternative to biopsy and spinal tape. However, obtaining molecular information of brain cells using non-invasive methods is challenging. In this research work, an automatic method of astrocytoma grading using Convolutional Neural Networks (CNN) has been proposed. Results have been validated on a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan. The proposed method proved a significant achievement in terms of accuracy as 99.06% (for astrocytoma of Grade-I), 94.01% (for astrocytoma of Grade-II), 95.31% (for astrocytoma of Grade-III), 97.85% (for astrocytoma of Grade-IV), and overall accuracy of 96.56%. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Astrocytoma is the most common and aggressive brain tumor, in its highest grade, the prognosis is ‘low survival rate’. Spinal tap and biopsy are the methods executed in order to determine the grade of astrocytoma. Once the grade of astrocytoma is determined, treatment is planned to improve the life expectancy of oncological subjects. Spinal tap and biopsy are invasive diagnostic procedures. Magnetic resonance imaging (MRI) being widely used imaging modality to detect brain tumors, produces the large volume of MRI data each moment in clinical environments. Automated and reliable methods of astrocytoma grading from the analysis of MRI images are required as an alternative to biopsy and spinal tape. However, obtaining molecular information of brain cells using non-invasive methods is challenging. In this research work, an automatic method of astrocytoma grading using Convolutional Neural Networks (CNN) has been proposed. Results have been validated on a locally developed dataset, obtained from Department of Radiology (Diagnostics), Bahawal Victoria Hospital, Bahawalpur, Pakistan. The proposed method proved a significant achievement in terms of accuracy as 99.06% (for astrocytoma of Grade-I), 94.01% (for astrocytoma of Grade-II), 95.31% (for astrocytoma of Grade-III), 97.85% (for astrocytoma of Grade-IV), and overall accuracy of 96.56%. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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Risk-free WHO grading of astrocytoma using convolutional neural networks from MRI images |
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Bajwa, Usama Ijaz Waraich, Mustansar Mahmood Anwar, Muhammad Waqas |
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