https://github.com/recker-dev/alzheimer-cnn-tinyvgg16

This project uses a TinyVGG16-based CNN to classify MRI scans for Alzheimer's Disease stages: Mild Impairment, Moderate Impairment, No Impairment, and Very Mild Impairment. It includes Jupyter notebooks for training and prediction, and a Streamlit app for easy inference. The model achieves high metrics in predicting Alzheimer's stages.
https://github.com/recker-dev/alzheimer-cnn-tinyvgg16

ai ai-in-healthcare alzeheimers cnn-classification deep-learning image-classification kaggle-dataset medical-imaging mri-images neuroimaging pytorch pytorch-cnn-classification streamlit streamlit-webapp tinyvgg

Last synced: 17 days ago
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This project uses a TinyVGG16-based CNN to classify MRI scans for Alzheimer's Disease stages: Mild Impairment, Moderate Impairment, No Impairment, and Very Mild Impairment. It includes Jupyter notebooks for training and prediction, and a Streamlit app for easy inference. The model achieves high metrics in predicting Alzheimer's stages.

• Host: GitHub
• URL: https://github.com/recker-dev/alzheimer-cnn-tinyvgg16
• Owner: Recker-Dev
• Created: 2024-11-26T05:05:36.000Z (27 days ago)
• Default Branch: main
• Last Pushed: 2024-11-26T05:30:45.000Z (27 days ago)
• Last Synced: 2024-11-26T06:26:34.674Z (27 days ago)
• Topics: ai, ai-in-healthcare, alzeheimers, cnn-classification, deep-learning, image-classification, kaggle-dataset, medical-imaging, mri-images, neuroimaging, pytorch, pytorch-cnn-classification, streamlit, streamlit-webapp, tinyvgg
• Language: Jupyter Notebook
• Homepage: https://alzheimer-cnn-tinyvgg16.streamlit.app/
• Size: 8.16 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# CNN Model for Alzheimer's MRI Dataset

This project implements a Convolutional Neural Network (CNN) for classifying MRI scans into various stages of Alzheimer's Disease using the [Best Alzheimer's MRI Dataset](https://www.kaggle.com/datasets/lukechugh/best-alzheimer-mri-dataset-99-accuracy/data).

## Dataset

The dataset comprises high-quality MRI scans labeled for Alzheimer's Disease progression. It can be downloaded from [Kaggle](https://www.kaggle.com/datasets/lukechugh/best-alzheimer-mri-dataset-99-accuracy/data).

### Problem Statement

The task is a **classification** problem to predict the stage of Alzheimer's Disease.

### Classes

The dataset includes the following classes:

- **Mild Impairment**

- **Moderate Impairment**

- **No Impairment**

- **Very Mild Impairment**

## Model Architecture

The CNN model comprises two convolutional blocks followed by a fully connected classifier; influenced by TinyVGG16 architecture.

## Performance Metrics

The model was evaluated on both training and test datasets. Below are the key metrics:

### Training Metrics

- **Loss:** 0.0039

- **Accuracy:** 99.90%

- **Precision:** 0.9990

- **Recall:** 0.9990

- **F1-Score:** 0.9990

### Testing Metrics

- **Loss:** 0.1574

- **Accuracy:** 95.47%

- **Precision:** 0.9563

- **Recall:** 0.9547

- **F1-Score:** 0.9548

### Train-Test Loss Over Epochs:

![Train Test Loss as observed over 20 epochs](outputs/train-test-loss-over-epochs.png "Train Test Loss")

### Train Data Confusion Matrix:

![Train Data Confusion Matrix](outputs/train_data_cnf_mat.png "Train Data Confusion Matrix")

### Test Data Confusion Matrix:

![Test Data Confusion Matrix](outputs/test_data_cnf_mat.png "Test Data Confusion Matrix")

## How to get the code

```bash

git clone https://github.com/Recker-Dev/alzheimer-cnn-tinyVGG16.git

cd alzheimer-cnn

```

## How to Setup Environment

Follow these steps to set up and run the project:

1. **Create a Conda environment**  

   Run the following command to create a new environment named `alz_torch_cnn` with Python 3.10:

   ```bash

   conda create --name alz_torch_cnn python=3.10 -y

   ```

2. **Activate the environment**

   ```bash

   conda activate alz_torch_cnn

   ```

3. **Install dependencies**

   ```bash

   pip install -r requirements.txt

   ```

4. **Deactivate the environment**

   ```bash

   conda deactivate

   ```

## How to Use the Model

You can interact with the model in two ways:

1. **Using Jupyter Notebooks**  

   There are notebooks provided under the `notebooks` section. These notebooks allow you to experiment with the model, visualize predictions, and understand its workings in a step-by-step manner.

2. **Using the Streamlit Application**  

   For users without much coding background, an application (`app.py`) is provided for easy inference. Follow these steps to use the app:

   - Activate the environment:

     ```bash

     conda activate alz_torch_cnn

     ```

   - Run the Streamlit app:

     ```bash

     streamlit run app.py

     ```

3. **Testing Images**  

   Testing images are provided in the `Sample Testing Alzheimer Dataset` folder. Use these images to test the model's performance through the Streamlit app or the provided notebooks.

## Folder Structure:

```

root

├── app.py        # Main Streamlit application for user interaction

├── models

│   └── alz_CNN.pt  # Trained PyTorch model for Alzheimer's classification

├── model_arch.py  # Defines the architecture of the Alzheimer's classification model

├── notebooks      # Jupyter notebooks for development and analysis

│   ├── Alzehmier_CNN.ipynb  # Notebook for training the Alzheimer's classification model

│   └── predict_using_alzehmierCNN.ipynb  # Notebook for demonstrating model prediction

└── Sample Testing Alzehimer Dataset  # Folder containing sample images for testing

    └── test            # Subfolder containing images for inference/testing

        ├── Mild Impairment

        ├── Moderate Impairment

        ├── No Impairment

        └── Very Mild Impairment

└── requirements.txt  # File listing required Python libraries

```