Brain Tumor Detection using Deep Learning
Automated MRI analysis for precise brain tumor detection and diagnosis
Project Overview
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How This System Works
This project develops a robust deep learning-based system for automated detection of brain tumors using MRI scans. By leveraging various machine learning approaches including supervised, unsupervised, and state-of-the-art deep learning models, we create an accurate and reliable diagnostic tool that can assist medical professionals in early detection and treatment planning.
Key Features & Capabilities
Multi-Model Approach
Supervised, unsupervised, and state-of-the-art deep learning models for comprehensive analysis
Advanced AI Models
Vision Transformers, U-Net, V-Net, and self-supervised learning approaches
High Accuracy Detection
Automated tumor detection with enhanced diagnostic accuracy and patient care
Comprehensive Dataset
Brain MRI Images dataset with tumor and non-tumor classifications
Methodologies & Approaches
Supervised Learning
- • Convolutional Neural Networks (CNNs)
- • Transfer Learning (ResNet, VGG, EfficientNet)
- • Ensemble methods
Unsupervised Learning
- • Autoencoders for anomaly detection
- • Clustering techniques
- • Dimensionality reduction
State-of-the-Art
- • Vision Transformers (ViT)
- • Advanced segmentation (U-Net, V-Net)
- • Self-supervised learning
Technical Implementation
The project is structured with comprehensive data processing, model training, and evaluation pipelines:
- Data Processing: Automated dataset download, preprocessing, and augmentation
- Model Training: Multiple training scripts for different approaches
- Evaluation: Comprehensive metrics and visualization tools
- Deployment: Ready-to-use prediction pipeline for new MRI scans
Dataset & Preprocessing
Data Source
- • Brain MRI Images for Brain Tumor Detection (Kaggle)
- • Categorized into tumor/non-tumor classes
- • Automated download via Kaggle API
- • 70%/15%/15% train/validation/test split
Preprocessing Pipeline
- • Image resizing to 224x224 pixels
- • Normalization and contrast enhancement
- • Data augmentation for training
- • Metadata generation and visualization
Project Structure
DeepLearning_for_BrainTumorDetection/ ├── data/ # Dataset storage │ ├── raw/ # Original dataset │ └── processed/ # Preprocessed data ├── notebooks/ # Jupyter notebooks for analysis ├── src/ # Source code │ ├── data_processing/ # Data loading and preprocessing │ ├── models/ # Model implementations │ ├── training/ # Training scripts │ └── evaluation/ # Model evaluation ├── results/ # Results and visualizations └── requirements.txt # Dependencies
Team Collaboration
Supervised Learning
Jessica, Jane - CNN and transfer learning approaches
Unsupervised Learning
Arcan, Diego - Autoencoders and clustering methods
State-of-the-Art
Sean, Vincent - Vision Transformers and advanced models
🔗 Project Repository
This project is open source and available on GitHub. The repository includes comprehensive documentation, Jupyter notebooks for analysis, and complete source code for all three learning approaches. Features automated data processing, model training pipelines, and evaluation metrics.