Advancements in hybrid approaches for brain tumor segmentation in MRI: a comprehensive review of machine learning and deep learning techniques

Abstract

Magnetic resonance imaging (MRI) brain tumour segmentation is essential for the diag nosis, planning, and follow-up of patients with brain tumours. In an efort to increase ef ciency and accuracy, a number of machine learning and deep learning algorithms have been developed over time to automate the segmentation process. Hybrid strategies, which include the advantages of both machine learning and deep learning, have become more and more popular as viable options. This in-depth analysis covers the developments in hybrid techniques for MRI segmentation of brain tumours. The essential ideas of machine learn ing and deep learning approaches are then covered, with an emphasis on their individual advantages and disadvantages. After that, the review explores the numerous hybrid strate gies put out in the literature. In hybrid approaches, various phases of the segmentation pipeline are combined with machine learning and deep learning techniques. Pre-process ing, feature extraction, and post-processing are examples of these phases. The paper exam ines at various combinations of methods utilised at these phases, such as segmentation using deep learning models and feature extraction utilising conventional machine learning algorithms. The implementation of ensemble approaches, which integrate forecasts from various models to improve segmentation accuracy, is also explored. The research study also examines the properties of freely accessible brain tumour datasets, which are essential for developing and testing hybrid models. To address the difculties of generalisation and robustness in brain tumour segmentation, it emphasises the necessity of vast, varied, and annotated datasets. Additionally, by contrasting them with conventional machine learning and deep learning techniques, the review analyses the efectiveness of hybrid approaches reported in the literature. This comprehensive research provides information on recent advancements in hybrid techniques for MRI segmenting brain tumours. It emphasises the potential for merging deep learning and machine learning methods to enhance the preci sion and efectiveness of brain tumour segmentation, ultimately assisting in improving patient diagnosis and treatment planning.