Automated Brain Tumor Diagnosis using Deep Learning: Segmentation, Detection, and Classification

Brain tumors are critical medical conditions that require correct identification and classification to facilitate appropriate treatment. This study explores deep learning techniques for improving brain tumor segmentation, detection and classification. Augmentation techniques handle problems in small and uneven datasets to enhance the reliability of models. Segmentation is performed using various architectures like U-net to identify the tumor area and Optimized CNNs were used as classification models to filter out tumors into types including glioma, meningioma, and pituitary tumors with high accuracy. The results indicate big improvements in diagnosing diseases hence; it gives the health workers reliable tools for finding the problems early and planning treatment. This study demonstrates how deep learning can improve medical imaging and make the clinical results better.