Brain Tumor Detection Project Guide

Brain tumors are life-threatening conditions that require early detection for effective treatment. However, manual analysis of MRI scans can be tedious and prone to human error, especially when detecting subtle abnormalities. With the advent of deep learning, convolutional neural networks (CNNs) have demonstrated outstanding capabilities in medical image analysis, offering faster, more accurate tumor detection. Building an AI-powered brain tumor detection system can assist doctors in diagnosis and save lives through early intervention.

By training convolutional neural networks on MRI scan datasets, deep learning models can classify brain images into tumor or non-tumor categories with high accuracy. Techniques like transfer learning using pre-trained models (ResNet, VGG, MobileNet) further enhance performance even with limited data. Data augmentation, regularization, and advanced optimizers ensure the model generalizes well across different patient scans, making this project a highly impactful application of AI in healthcare.

The system processes MRI brain scan images, normalizes them, and applies data augmentation techniques for better generalization. A CNN model is trained to extract features such as edges, textures, and intensity patterns, which help differentiate between healthy tissue and tumors. After training, the model predicts the presence of tumors on unseen MRI scans with high confidence, supporting radiologists in their decision-making process while reducing workload and diagnosis time.

• Collect a labeled MRI brain scan dataset with tumor and non-tumor images.
• Preprocess images: resize, normalize, and apply augmentation (rotation, flip, zoom) techniques.
• Train a CNN model or fine-tune a pre-trained model like VGG16, ResNet50, or MobileNet.
• Evaluate model accuracy using Precision, Recall, F1-score, and ROC-AUC metrics.
• Deploy the trained model into a web application for real-time MRI scan analysis and diagnosis support.

• Brain MRI Images Dataset (Kaggle)
• TensorFlow Transfer Learning Guide
• Keras CNN Image Classification Guide