Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper
Segmentation is crucial for brain gliomas as it delineates the glioma’s extent and location, aiding in precise treatment planning and monitoring, thus improving patient outcomes. Accurate segmentation ensures proper identification of the glioma’s size and position, transforming images into applicabl...
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| Format: | Article |
| Language: | English |
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Bulgarian Academy of Sciences
2025-06-01
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| Series: | International Journal Bioautomation |
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| Online Access: | http://www.biomed.bas.bg/bioautomation/2025/vol_29.2/files/29.2_03.pdf |
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| author | Kiranmayee Janardhan Vinay Martin D’Sa Prabhu T. Christy Bobby |
| author_facet | Kiranmayee Janardhan Vinay Martin D’Sa Prabhu T. Christy Bobby |
| author_sort | Kiranmayee Janardhan |
| collection | DOAJ |
| description | Segmentation is crucial for brain gliomas as it delineates the glioma’s extent and location, aiding in precise treatment planning and monitoring, thus improving patient outcomes. Accurate segmentation ensures proper identification of the glioma’s size and position, transforming images into applicable data for analysis. Classification of brain gliomas is also essential because different types require different treatment approaches. Accurately classifying brain gliomas by size, location, and aggressiveness is essential for personalized prognosis prediction, follow-up care, and monitoring disease progression, ensuring effective diagnosis, treatment, and management. In glioma research, irregular tissues are often observable, but error-free and reproducible segmentation is challenging. Many researchers have surveyed brain glioma segmentation, proposing both fully automatic and semi-automatic techniques. The adoption of these methods by radiologists depends on ease of use and supervision, with semi-automatic techniques preferred due to the need for accurate evaluations. This review evaluates effective segmentation and classification techniques post-magnetic resonance imaging acquisition, highlighting that convolutional neural network architectures outperform traditional techniques in these tasks. |
| format | Article |
| id | doaj-art-2cdc21f937614b57afec52591c8a2af5 |
| institution | Kabale University |
| issn | 1314-1902 1314-2321 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Bulgarian Academy of Sciences |
| record_format | Article |
| series | International Journal Bioautomation |
| spelling | doaj-art-2cdc21f937614b57afec52591c8a2af52025-08-20T03:31:52ZengBulgarian Academy of SciencesInternational Journal Bioautomation1314-19021314-23212025-06-0129214516610.7546/ijba.2025.29.2.001022Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review PaperKiranmayee Janardhan0Vinay Martin D’Sa PrabhuT. Christy BobbyDepartment of Electronics and Communications, Ramaiah University of Applied Sciences, 4th Phase, Peenya, Bengaluru, Karnataka, India Segmentation is crucial for brain gliomas as it delineates the glioma’s extent and location, aiding in precise treatment planning and monitoring, thus improving patient outcomes. Accurate segmentation ensures proper identification of the glioma’s size and position, transforming images into applicable data for analysis. Classification of brain gliomas is also essential because different types require different treatment approaches. Accurately classifying brain gliomas by size, location, and aggressiveness is essential for personalized prognosis prediction, follow-up care, and monitoring disease progression, ensuring effective diagnosis, treatment, and management. In glioma research, irregular tissues are often observable, but error-free and reproducible segmentation is challenging. Many researchers have surveyed brain glioma segmentation, proposing both fully automatic and semi-automatic techniques. The adoption of these methods by radiologists depends on ease of use and supervision, with semi-automatic techniques preferred due to the need for accurate evaluations. This review evaluates effective segmentation and classification techniques post-magnetic resonance imaging acquisition, highlighting that convolutional neural network architectures outperform traditional techniques in these tasks.http://www.biomed.bas.bg/bioautomation/2025/vol_29.2/files/29.2_03.pdfbrain glioma analysisclassificationdeep learningsegmentation techniquesmagnetic resonance imaging |
| spellingShingle | Kiranmayee Janardhan Vinay Martin D’Sa Prabhu T. Christy Bobby Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper International Journal Bioautomation brain glioma analysis classification deep learning segmentation techniques magnetic resonance imaging |
| title | Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper |
| title_full | Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper |
| title_fullStr | Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper |
| title_full_unstemmed | Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper |
| title_short | Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper |
| title_sort | comparative evaluation of traditional methods and deep learning for brain glioma imaging review paper |
| topic | brain glioma analysis classification deep learning segmentation techniques magnetic resonance imaging |
| url | http://www.biomed.bas.bg/bioautomation/2025/vol_29.2/files/29.2_03.pdf |
| work_keys_str_mv | AT kiranmayeejanardhan comparativeevaluationoftraditionalmethodsanddeeplearningforbraingliomaimagingreviewpaper AT vinaymartindsaprabhu comparativeevaluationoftraditionalmethodsanddeeplearningforbraingliomaimagingreviewpaper AT tchristybobby comparativeevaluationoftraditionalmethodsanddeeplearningforbraingliomaimagingreviewpaper |