A novel hybrid model for brain tumor classification leveraging U-Net segmentation and ResNet50 architecture

Abstract

Brain tumors are life-threatening conditions requiring accurate and timely diagnosis for effective treatment. This paper proposes a novel hybrid model combining U-Net for tumor segmentation and residual network 50 (ResNet50) architecture for classification to achieve performance in brain tumor classification from magnetic resonance imaging (MRI) images. This paper proposes a novel hybrid model that integrates U-Net for tumor segmentation with ResNet50 architecture for classification, enabling robust multi-class classification across glioma, meningioma, pituitary tumor, and no tumor classes. Utilizing a diverse dataset of 7,023 MRI images, the model achieves a remarkable accuracy of 99.78±0.05%, outperforming existing methods. Compared to related works, the proposed model demonstrates superior accuracy and scalability. This hybrid approach addresses key challenges in medical imaging, providing a robust and interpretable solution for real-world clinical applications.