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

2026-03-05

AI Summary

Gliomas exhibit highly irregular morphologies, making their precise segmentation and classification critical for treatment planning and prognostic assessment; however, achieving automated, reproducible, and error-free analysis remains challenging. This study systematically reviews magnetic resonance imaging–based approaches for glioma segmentation and classification, offering a comprehensive comparison between traditional image processing algorithms and deep learning models, with particular emphasis on the performance of convolutional neural network (CNN) architectures. The findings demonstrate that CNNs significantly outperform conventional methods in both segmentation accuracy and classification precision, offering enhanced automation and greater clinical applicability. These advances underscore the potential of CNN-based frameworks to serve as reliable technical foundations for intelligent computer-aided diagnosis and treatment support in neuro-oncology.

Technology Category

Computer Vision: SegmentationMachine Learning: Multi-class/Multi-label Learning & Extreme ClassificationCognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphsWeb Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web textSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• brain glioma
• segmentation
• classification
• magnetic resonance imaging
• tumor heterogeneity

Innovation

• convolutional neural network
• brain glioma segmentation
• deep learning
• medical image classification
• semi-automatic segmentation