[2603.04796] Comparative Evaluation of Traditional Methods and Deep Learning for Brain Glioma Imaging. Review Paper

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.04796 (cs) [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-auto...