Enhancing precision medicine in neuroimaging: hybrid model for brain tumor analysis

Abstract

Brain tumors are a significant health challenge requiring precise diagnostic methods for optimal patient care. This study introduces a novel approach utilizing a convolutional neural network-based gated recurrent unit (CNN-GRU) for brain tumor detection. The method encompasses a rigorous preprocessing pipeline tailored for multi-modal magnetic resonance imaging (MRI) images, focusing on standardizing dimensions, normalizing pixel values, and enhancing contrast to facilitate robust tumor identification. Subsequently, temporal sequences of preprocessed images are analyzed by the CNN-GRU network to accurately pinpoint tumor regions. Evaluation on the BraTS2020 dataset, comprising diverse MRI scans with manual annotations, demonstrates the method's robust performance in tumor detection, reflecting real-world clinical complexities. Through meticulous preprocessing and model optimization, the approach achieves a remarkable accuracy rate of 99%, offering crucial insights for clinicians in treatment planning and prognosis prediction. Implemented using Python, the framework contributes to advancing brain tumor diagnosis and decision support systems, potentially enhancing personalized medicine and clinical practice. By improving diagnostic accuracy and patient outcomes, this research underscores the importance of integrating advanced computational techniques with medical imaging to address critical healthcare challenges effectively.