Alzheimer's Disease Detection using Convolutional Neural Networks

A deep learning-based approach for detecting and classifying different stages of Alzheimer's disease using MRI brain scans. This project implements a Convolutional Neural Network (CNN) to classify brain MRI images into four categories representing different stages of cognitive decline.

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🧠 Project Overview

Alzheimer's disease is a progressive brain disorder where early diagnosis is critical for effective management. This project leverages Artificial Intelligence (AI) to build a powerful diagnostic tool that can automatically analyze and interpret brain MRI scans, providing a fast, non-invasive, and reliable aid for medical professionals.

🎯 Objectives

• Develop and implement a Convolutional Neural Network (CNN) model capable of accurately detecting Alzheimer's disease from MRI scans
• Train the CNN model on a dataset of MRI images to learn the key features associated with different stages of Alzheimer's
• Create a classification system that can effectively classify brain MRI images into categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented
• Evaluate model performance and achieve high levels of accuracy, precision, and recall in detecting the disease
• Build a practical tool that can potentially assist radiologists and doctors in the early and accurate diagnosis of Alzheimer's disease, leading to better patient outcomes

📊 Key Features

• Multi-class Classification: Classifies MRI scans into 4 distinct stages of Alzheimer's disease
• High Accuracy: Achieves 99.2% validation accuracy on test dataset
• Fast Inference: ~10ms per image prediction
• Efficient Training: Complete training in under 3 minutes
• Deployable Model: Saved as portable .h5/Keras file for real-world integration
• Comprehensive Evaluation: Multiple metrics including accuracy, loss plots, confusion matrix, and classification reports

🏗️ Model Architecture

CNN Design

The model consists of three convolutional blocks for feature extraction, followed by dense layers and Softmax activation for classification:

1. Convolutional Block 1: Conv2D + ReLU + MaxPooling2D
2. Convolutional Block 2: Conv2D + ReLU + MaxPooling2D
3. Convolutional Block 3: Conv2D + ReLU + MaxPooling2D
4. Dropout Layer: Regularization to prevent overfitting
5. Flatten Layer: Converts 2D features to 1D vector
6. Dense Layers: Fully connected layers with Softmax activation (4 classes)

Workflow Diagram

graph TB
    subgraph DataPrep["1. Data Preparation"]
        A[Input: Alzheimer's MRI Scans<br/>10,240 images 128x128px] --> B[Train-Test Split<br/>80% Train / 20% Validation]
        B --> C[Image Normalization &<br/>Augmentation]
    end
    
    subgraph ModelArch["2. Model Architecture"]
        D[CNN Definition<br/>Keras Sequential]
        
        subgraph Layers
            E[Conv2D Block 1<br/>+ ReLU + MaxPool] --> F[Conv2D Block 2<br/>+ ReLU + MaxPool]
            F --> G[Conv2D Block 3<br/>+ ReLU + MaxPool]
            G --> H[Dropout]
            H --> I[Flatten]
            I --> J[Dense + Softmax<br/>4 classes]
        end
        
        D --> E
    end
    
    subgraph Training["3. Training Pipeline"]
        K[Model Compilation<br/>Adam Optimizer<br/>Categorical Crossentropy]
        L[Model Training<br/>15 Epochs, Batch Size: 32<br/>~10 sec/epoch]
    end
    
    subgraph Evaluation["4. Evaluation & Output"]
        M[Model Evaluation]
        
        subgraph Metrics
            N[Accuracy: 99.2%<br/>Loss Plots]
            O[Classification Report<br/>Precision, Recall, F1]
            P[Confusion Matrix]
        end
        
        M --> N
        M --> O
        M --> P
        
        Q[Output: Saved Model<br/>.h5 file<br/>Inference: ~10ms/image]
    end
    
    DataPrep --> ModelArch
    ModelArch --> Training
    Training --> Evaluation
    Training -.Backpropagation<br/>Weight updates.-> ModelArch

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📈 Performance Metrics

Training Performance

• Dataset Size: 10,240 MRI Scans (128×128 pixels)
• Batch Size: 32
• Epochs: 15
• Average Time per Epoch: ~10 seconds
• Total Training Time: 2 minutes 50 seconds

Hardware Utilization

• GPU: NVIDIA RTX 3070 Ti (8GB VRAM)
• Peak GPU Utilization: 98%
• Peak VRAM Usage: 5.4 GB / 8.0 GB

Model Performance

• Final Validation Accuracy: 99.2%
• Validation Set Size: 100 images
• Inference Time: ~10ms per image

🛠️ Technologies Used

• Python: Core programming language
• TensorFlow/Keras: Deep learning framework
• NumPy: Numerical computations
• Pandas: Data manipulation
• Matplotlib/Seaborn: Data visualization
• scikit-learn: Metrics and evaluation

📋 Requirements

tensorflow>=2.0.0
keras>=2.0.0
numpy>=1.19.0
pandas>=1.0.0
matplotlib>=3.0.0
seaborn>=0.11.0
scikit-learn>=0.24.0

🚀 Getting Started

Installation

1. Clone the repository:

git clone https://github.com/Bugaddr/alzheimer_detection.git
cd alzheimer_detection

2. Install required packages:

pip install -r requirements.txt

Dataset Preparation

Organize your MRI images into the following structure:

data/
├── train/
│   ├── NonDemented/
│   ├── VeryMildDemented/
│   ├── MildDemented/
│   └── ModerateDemented/
└── test/
    ├── NonDemented/
    ├── VeryMildDemented/
    ├── MildDemented/
    └── ModerateDemented/

Usage

1. Run the notebook: Open and execute alzheimer_detection.py
2. Data Preprocessing: Images are normalized and split into 80% training and 20% validation
3. Model Training: Train the CNN for 15 epochs (~3 minutes on RTX 3070 Ti)
4. Evaluation: View comprehensive metrics and classification reports
5. Save Model: Export trained model as .h5 file

# Example: Load and use the trained model
from tensorflow import keras
import numpy as np
from PIL import Image

# Load the trained model
model = keras.models.load_model('alzheimer_cnn_model.h5')

# Prepare an image for prediction
img = Image.open('path/to/mri_scan.jpg')
img = img.resize((128, 128))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)

# Make prediction
prediction = model.predict(img_array)
classes = ['NonDemented', 'VeryMildDemented', 'MildDemented', 'ModerateDemented']
predicted_class = classes[np.argmax(prediction)]
confidence = np.max(prediction) * 100

print(f"Prediction: {predicted_class}")
print(f"Confidence: {confidence:.2f}%")

📁 Project Structure

alzheimer_detection/
│
├── alzheimer_detection.ipynb    # Main Jupyter notebook
├── README.md                     # Project documentation
├── requirements.txt              # Python dependencies
├── models/                       # Saved model files
│   └── alzheimer_cnn_model.h5
├── data/                         # Dataset directory
│   ├── train/
│   └── test/
└── results/                      # Output plots and reports
    ├── accuracy_plot.png
    ├── loss_plot.png
    ├── confusion_matrix.png
    └── classification_report.txt

📚 Literature Review

Our approach is informed by recent advances in deep learning for Alzheimer's detection:

1. Al-Mourabit et al. (2021) - Confirmed CNNs as state-of-the-art for AD detection, highlighting challenges like limited datasets and model interpretability
2. Suk et al. (2014) - Demonstrated deep feature learning superiority over handcrafted features for AD/MCI classification
3. Payan & Montana (2015) - Pioneered 3D CNNs for capturing spatial brain context in neuroimaging
4. Falahati et al. (2014) - Established MRI-based biomarkers using classical machine learning approaches
5. Li et al. (2019) - Used hippocampal MRI analysis for early AD prediction

🌟 Impact & Implications

Medical Impact

• Accelerates Early Diagnosis: Empowers clinicians with an AI-powered tool to detect Alzheimer's disease at its earliest stages, critical for effective patient management
• Improves Diagnostic Accuracy & Efficiency: Acts as a reliable "second opinion" for medical experts, reducing human error and speeding up diagnostic workflows
• Enhances Patient Outcomes: Earlier, more accessible diagnosis allows patients and families crucial time to plan care, explore treatments, and improve quality of life

Technical Impact

• Scalable AI Foundation: Serves as a blueprint for developing similar AI-driven diagnostic tools for other neurological disorders
• Proof of Concept: Demonstrates successful application of deep learning for medical image analysis
• Real-World Deployment Ready: Portable model format enables integration into existing healthcare systems

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 References

[1] C. Al-Mourabit, M. Abdel-Nasser, and A. Valls, "A literature review of deep learning in Alzheimer's disease," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 1, pp. 1–14, 2021.

[2] H.-I. Suk, S.-W. Lee, and D. Shen, "Deep learning-based feature representation for AD/MCI classification," in Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2014, pp. 583–590.

[3] A. Payan and G. Montana, "Predicting Alzheimer's disease: A neuroimaging study with 3D convolutional neural networks," arXiv preprint arXiv:1502.02506, 2015.

[4] F. Falahati, E. Westman, and A. Simmons, "Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging," Journal of Alzheimer's Disease, vol. 41, no. 3, pp. 685–708, 2014.

[5] H. Li, M. Habes, D. A. Wolk, and Y. Fan, "A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging," Alzheimer's & Dementia, vol. 15, no. 8, pp. 1059–1070, 2019.

⚠️ Disclaimer

This project is for educational and research purposes only. It should not be used as a substitute for professional medical diagnosis. The model is a proof-of-concept and has not been clinically validated. Always consult with qualified healthcare professionals for medical advice and diagnosis.

📜 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Dataset providers and the medical imaging community
• TensorFlow and Keras development teams
• Research community working on Alzheimer's disease detection
• Department of Electronics & Communication Engineering, Ramdeobaba College of Engineering and Management

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Note: Early detection of Alzheimer's disease can significantly improve patient outcomes. This project demonstrates the potential of AI-assisted medical diagnostics and aims to contribute to advancing healthcare technology.