Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping.

ABSTRACT: In the diagnosis and treatment of brain tumors, the automatic classification and segmentation of medical images play a pivotal role. Early detection facilitates timely intervention, significantly improving patient survival rates. This study introduces a novel method for the automated classification and segmentation of brain tumors, aiming to enhance both diagnostic accuracy and efficiency. Magnetic Resonance (MR) imaging remains the gold standard in clinical brain tumor diagnostics; however, it is a time-intensive and labor-intensive process. Consequently, the integration of automated detection, localization, and classification methods is not only desirable but essential. In this research, we present a novel framework that enables both tumor classification and post-classification diagnosti

SUBMITTER: Zarenia E 

PROVIDER: S-EPMC11890586 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature