D-Shepard-p1-57

🧠 Shepard's Gift: A Deep Learning Model for Brain Tumor Classification

"It's a beautiful day to save lives."
Inspired by Dr. Derek Shepherd from Grey's Anatomy — a neurosurgeon who never backed down from the impossible.

Shepard's Gift is a deep learning initiative to assist in diagnosing brain tumors using MRI scans. This repository contains Phase 1: Classification, where a custom Convolutional Neural Network (CNN) classifies brain MRI images into four categories:

• ✅ Glioma
• ✅ Meningioma
• ✅ Pituitary Tumor
• ✅ No Tumor

With meticulous optimization, the model achieves a validation accuracy of 98.25%, demonstrating high precision and clinical potential.

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📊 Model Performance

| Metric and Values |
| Validation Accuracy | 98.25%
| Test Accuracy | 98.25%
| Optimizer | Adam
| Learning Rate | 0.0001
| Dropout Rate | 0.5
| Early Stopping | Yes (patience=10)
| Model Checkpoint | Yes (monitors val_loss)

📈 Learning Curves

• Smooth convergence with minimal overfitting.
• Validation metrics closely follow training curves before divergence triggers early stopping.

🤝 Confusion Matrix

• High precision and recall across all classes.
• Very few misclassifications — especially strong at identifying "No Tumor" cases.

📊 Visualizations are saved in the plots/ directory after training.

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✨ Key Features

• High Accuracy: Reproducible 98.25% accuracy on test data.
• Custom CNN Architecture: Built from scratch using TensorFlow & Keras (4 convolutional blocks).
• Hyperparameter Optimization: Grid search with interactive 3D visualization (Plotly).
• Robust Training: EarlyStopping and ModelCheckpoint prevent overfitting.
• Reproducibility: Fixed random seeds for TensorFlow, NumPy, and Python.
• Well-Documented: Includes training logs, plots, and step-by-step instructions.

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💾 Dataset & Setup

📁 Dataset Source

This project uses the Brain Tumor MRI Dataset from Kaggle.

🗂️ Required Folder Structure

After downloading and extracting the dataset, organize your project like this:

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🛠️ Technology Stack

Component
| Framework -> TensorFlow, Keras
| Language -> Python 3.8+ |
| Libraries -> Matplotlib, Seaborn, Scikit-learn, Plotly, NumPy |
| Hardware -> NVIDIA RTX 3050 (laptop gpu), CUDA & cuDNN |
| Visualization -> Interactive 3D plots with Plotly |

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🔮 Future Work: Phase 2 — Tumor Segmentation

The next phase will implement a U-Net architecture for image segmentation, enabling:

• Precise tumor boundary detection
• Volumetric analysis
• Surgical planning assistance
• This will transform Shepard's Gift from a classifier into a clinical decision-support tool for neurosurgeons.

❤️ Dedicated to every patient, doctor, and researcher fighting brain tumors. Let’s make the impossible, possible.