Brain Tumor Image Classification Using Deep Learning

Brain tumors represent a severe medical condition that can lead to significant neurological damage or death if not diagnosed early. Traditional diagnostic methods, though effective, often involve time-consuming processes and require expert interpretation. In this project, we propose a deep learning-based approach to automate brain tumor detection using magnetic resonance imaging (MRI) scans. The system leverages the VGG19 convolutional neural network architecture, pre-trained on ImageNet and further fine-tuned for binary classification of MRI images into “Tumor” and “No Tumor” categories. The model architecture is enhanced with additional dense layers to improve its ability to learn complex patterns specific to medical imaging.The classification model is deployed within a Flask-powered web application, enabling users to upload brain scan images and receive classification results in real-time. The image is preprocessed, resized to a fixed dimension, and passed through the trained model for prediction. The application aims to provide an accessible tool for preliminary tumor screening, particularly in scenarios where immediate medical expertise may not be available. This integration of deep learning and web development demonstrates the potential for AI-driven tools to supplement healthcare workflows, reduce diagnostic delays, and support clinical decision-making.It serves as a foundational step toward building scalable, intelligent medical diagnostic tools that prioritize both performance and accessibility