Improving Brain Tumor Classification and Segmentation Using ResUNet50

Brain tumors pose significant medical challenges due to their high recurrence rates. Integrating artificial intelligence, particularly deep learning, into healthcare enhances tumor classification, treatment prediction, and workflow efficiency. Deep learning-based segmentation has emerged as a crucial tool in medical imaging for assessing tumor characteristics. The experimental results demonstrate the robustness of ResUNet50 in medical image analysis for classification and segmentation, thus outperforming existing deep learning models. Thus, achieving an accuracy of 97% on the current dataset, ResNet50 proves to be a highly reliable tool for brain tumor analysis.