A Hybrid Res-BRNet Architecture for Efficient and Accurate Brain Tumor Classification

The growing presence of brain tumors emphasizes in the field of neuro-oncology the need of exact and efficient diagnostic tools. An indispensable tool for brain tumor identification is Magnetic Resonance Imaging (MRI). Yet, the complexity of manually interpreting MRI scans results from the variation of tumors. This paper offers Res-BRNet, a novel hybrid deep learning architecture that blends residual networks with regional convolutional neural networks (CNNs) to improve brain tumor detection, therefore addressing these problems. By means of deep residual learning and regional CNNs, Res-BRNet aims to improve feature extraction and generalization. After training on a large collection of MRI images, the model was evaluated using important measures like F1 score, accuracy, precision, recall, and sensitivity. Res-BRNet shows great accuracy, precision, and F1 scores in the experimental findings showing 98.83% in the classifying of gliomas, meningiomas, and pituitary tumors. Data augmentation methods help to efficiently lower overfitting, so improving model generalization. This work advances automated brain tumor detection systems by stressing the capacity of deep learning to enhance clinical environment diagnosis decision-making. The proposed Res-BRNet model is a significant asset for neuro-oncology, demonstrating substantial potential to enhance patient outcomes in the detection and treatment planning of brain tumors.