Brain Tumor Classification & Segmentation Using Deep Learning

Brain Tumor Classification & Segmentation Using Deep Learning This project focuses on applying Deep Learning techniques for automated brain tumor classification and segmentation from MRI images. The system leverages state-of-the-art Convolutional Neural Networks (CNNs) to accurately identify tumor types and precisely delineate tumor regions, supporting more reliable medical image analysis. By combining advanced image preprocessing, feature extraction, and deep neural network architectures, the model achieves high-performance tumor detection while providing detailed segmentation masks. These outputs can assist healthcare professionals in preoperative planning, treatment assessment, and disease monitoring. The project covers the complete deep learning pipeline, including: Medical image preprocessing and data augmentation. Brain tumor classification using pretrained CNN architectures. Pixel-level tumor segmentation for accurate tumor boundary detection. Model training, evaluation, and performance comparison using standard medical imaging metrics. Visualization of classification results and segmentation outputs for clinical interpretation. The system demonstrates how modern deep learning techniques can improve the accuracy, efficiency, and consistency of brain tumor analysis, providing valuable decision support for medical professionals.