This repository contains a machine learning model designed for the classification of Alzheimer's disease across multiple stages. The model utilizes various features extracted from brain imaging data to predict the stage of Alzheimer's disease a patient may be in. This README file provides an overview of the model, its usage, and additional information.
Alzheimer's disease is a neurodegenerative disorder characterized by progressive cognitive decline. Early diagnosis and staging of Alzheimer's are crucial for effective management and treatment planning. This project aims to develop a machine learning model capable of accurately classifying Alzheimer's disease across multiple stages based on brain imaging data.
To run the model, you will need the following dependencies:
- Python 3.x
- TensorFlow
- Keras
- NumPy
- Pandas
- Matplotlib
- scikit-learn
The dataset used for training and evaluation is not included in this repository due to its large size and privacy concerns. However, you can obtain the dataset from [source link] and preprocess it according to your requirements.
The model architecture consists of a convolutional neural network (CNN) followed by fully connected layers. The CNN extracts relevant features from brain imaging data, which are then fed into the fully connected layers for classification. The model is trained using a multi-class classification approach to predict the stage of Alzheimer's disease.
The model achieves an accuracy of 84% on the test set and demonstrates promising performance in classifying Alzheimer's disease across multiple stages. For detailed evaluation metrics and analysis, refer to the results directory.
This project was supervised by Dr. Eman Ayman & Eng. Samer Alaa, who provided invaluable guidance and expertise throughout its development as a part of the CDSS course at Cairo University Faculty of Engineering.
