SP
BravenNow
Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper
| USA | technology | ✓ Verified - arxiv.org

Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper

#brain glioma #deep learning #MRI #convolutional neural networks #medical imaging #glioma segmentation #traditional methods #diagnostic accuracy

📌 Key Takeaways

  • Traditional imaging methods like MRI and CT scans are standard for glioma detection but have limitations in accuracy and detail.
  • Deep learning techniques, particularly convolutional neural networks (CNNs), show superior performance in glioma segmentation and classification.
  • Integration of deep learning with traditional imaging can enhance diagnostic precision and treatment planning for brain gliomas.
  • The review highlights the need for more standardized datasets and validation to improve deep learning model reliability in clinical settings.

📖 Full Retelling

arXiv:2603.04796v1 Announce Type: cross Abstract: 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 cla

🏷️ Themes

Medical Imaging, Artificial Intelligence

📚 Related People & Topics

Deep learning

Deep learning

Branch of machine learning

In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" t...

View Profile → Wikipedia ↗
Magnetic resonance imaging

Magnetic resonance imaging

Medical imaging technique

Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to generate pictures of the anatomy and the physiological processes inside the body. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to form images of the organs in the body. MRI doe...

View Profile → Wikipedia ↗

Entity Intersection Graph

Connections for Deep learning:

🌐 Explainable artificial intelligence 3 shared
🌐 Medical imaging 2 shared
🌐 Applications of artificial intelligence 1 shared
🌐 Plant pathology 1 shared
🌐 Unmanned aerial vehicle 1 shared
View full profile

Mentioned Entities

Deep learning

Deep learning

Branch of machine learning

 Magnetic resonance imaging

Magnetic resonance imaging

Medical imaging technique
}
Original Source
--> Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04796 [Submitted on 5 Mar 2026] Title: Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper Authors: Kiranmayee Janardhan , Vinay Martin DSa Prabhu , T. Christy Bobby View a PDF of the paper titled Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper, by Kiranmayee Janardhan and 2 other authors View PDF Abstract: 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. Comments: 22 pages, 4 Figures Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04796 [cs.CV] (or arXiv:2603.04796v1 [cs.CV] for this ver...
Read full article at source

Source

arxiv.org

More from USA

News from Other Countries

🇬🇧 United Kingdom

🇺🇦 Ukraine