Advancements in deep learning techniques for brain tumor segmentation: A survey

Abstract

The escalating incidence of accurate detection of brain tumors within the discipline of neuro-oncology underscores the pressing demand for enhanced diagnostic methodologies. The extant corpus of literature, predominantly focused on the categorization of MRI images, is deficient in comprehensive solutions for the myriad challenges faced in the segmentation of brain tumors, including imaging anomalies, the nuanced delineation of tumor margins, tumor heterogeneity, and classification ambiguities. The aim of this research endeavor is to address these challenges by proposing a novel deep learning framework that integrates the renowned U-Net architecture with self-attention mechanisms, meticulously tailored for the segmentation of brain tumors. Our study rigorously evaluates and contrasts prevailing deep learning techniques, with an emphasis on the efficacy of the U-Net architecture in discerning both specific and generalized features within three-dimensional brain imaging. The integration of self-attention mechanisms is demonstrably effective in augmenting segmentation accuracy by directing focus towards pivotal tumor regions and enhancing overall precision. Principal findings reveal that our proposed model surpasses recent developments in brain tumor segmentation from the years 2020–2024 in metrics of accuracy, precision, sensitivity, and specificity. Significant conclusions indicate that this amalgamation establishes a novel benchmark in medical image segmentation, possessing the capacity to revolutionize diagnostic capabilities and therapeutic strategies. The ramifications extend beyond academic discourse, offering a glimmer of optimism for patients and healthcare professionals alike in the precise diagnosis and management of brain tumors.