Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

ABSTRACT: Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our appro

SUBMITTER: Gunasekaran S 

PROVIDER: S-EPMC11349112 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature