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

TL;DR

This review compares traditional and deep learning methods for brain glioma imaging, highlighting that CNN architectures outperform traditional techniques in segmentation and classification tasks, which are vital for diagnosis and treatment planning.

Contribution

The paper provides a comprehensive comparison of traditional and deep learning methods for glioma segmentation and classification, emphasizing CNN superiority.

Findings

CNN architectures outperform traditional methods in segmentation

Deep learning techniques improve classification accuracy

Semi-automatic methods are preferred for clinical use

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.