This repository contains an Alzheimer's Disease prediction model built using the NASNet Large architecture. The model is trained to analyze medical imaging data and predict the likelihood of a patient developing Alzheimer's Disease.
Alzheimer's Disease is a progressive neurological disorder that affects memory, thinking, and behavior. Early diagnosis and prediction of Alzheimer's Disease can greatly help in providing timely intervention and treatment to patients. This repository provides a deep learning model based on the NASNet Large architecture to predict the likelihood of a patient developing Alzheimer's Disease.
The model is trained on a dataset of medical imaging data, specifically brain MRI scans, collected from patients with and without Alzheimer's Disease. The dataset used in this project is not included in this repository due to privacy and licensing restrictions. However, you can obtain similar datasets from publicly available sources or by collaborating with research institutions and obtaining appropriate permissions.
The NASNet Large architecture is a convolutional neural network (CNN) designed specifically for image classification tasks. It is known for its excellent performance on various image classification benchmarks. The model consists of a series of convolutional and pooling layers, followed by fully connected layers for classification. The weights of the model are pretrained on a large-scale dataset and fine-tuned on the Alzheimer's Disease dataset provided.
To use this Alzheimer's Disease prediction model, follow these steps:
-
Clone this repository to your local machine. bash git clone https://github.com/your-username/alzheimers-prediction-nasnet-large.git
-
Install the required dependencies. It is recommended to use a virtual environment. bash cd alzheimers-prediction-nasnet-large pip install -r requirements.txt
-
Preprocess your brain MRI scans into the appropriate format required by the model. Ensure that your data is organized in a suitable directory structure.
-
Train the model using the preprocessed data. Adjust the hyperparameters and training configurations as per your requirements. bash python train.py
-
After training, you can use the trained model to make predictions on new brain MRI scans. Adjust the prediction script according to your needs. bash python predict.py --image path/to/brain_mri_scan.jpg
The model achieves an accuracy of XX% on the Alzheimer's Disease prediction task. It should be noted that the performance of the model may vary depending on the quality and size of the dataset used for training.
Contributions to this repository are welcome. If you encounter any issues or have suggestions for improvements, please open an issue or submit a pull request. Any help to enhance the model's performance or extend its functionality will be highly appreciated.
This project is licensed under the MIT License. You are free to use, modify, and distribute the code for academic and commercial purposes. However, the model's predictions should not be considered a substitute for professional medical advice or diagnosis.