Alzheimer-Detection

This project aims to develop an Alzheimer's disease detection model using deep learning. Unlike existing algorithms with approximately 91% accuracy, our model achieves an accuracy ranging from 93% to 94%.

Overview

Alzheimer's disease is a neurodegenerative disorder that affects memory and cognitive function. Early detection is crucial for effective intervention and treatment. This project leverages deep learning techniques to build a robust model for Alzheimer's disease detection.

Dataset

The dataset used for training and evaluation is stored in Google Drive. It consists of images categorized into different classes, each representing a specific class related to Alzheimer's disease.

ADNI Dataset

In addition to the primary dataset, this project incorporates data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. ADNI provides valuable neuroimaging and clinical data, enhancing the model's capability to learn from a diverse range of sources.

Project Structure

• Data Exploration: Randomly displays images from the dataset for visual inspection.
• Model Training: Constructs and trains a convolutional neural network (CNN) for Alzheimer's disease detection.
• Model Evaluation: Evaluates the model on a validation set and prints accuracy and loss metrics.
• Results Visualization: Plots the training history to visualize the model's performance over epochs.

Dependencies

• Python 3.x

• TensorFlow

• Matplotlib

• NumPy

• pandas

• Google Colab (for execution in a Colab environment)

• Future Work Continued research and improvement can further enhance the model's accuracy and generalization. Potential areas for exploration include:

Hyperparameter tuning Transfer learning with pre-trained models Larger and more diverse datasets Feel free to contribute to the project or use the code as a foundation for your own Alzheimer's disease detection models.

References

1. Alzheimer's Disease Neuroimaging Initiative (ADNI). Website: ADNI Website

2. F M JAVED MEHEDI SHAMRAT 1, SHAMIMA AKTER (2023). "AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images" *IEEE Research Paper

Dataset Link

https://drive.google.com/drive/folders/1lrX-lB4DXjNeez9_joRmxOOt1LFAqnb4?usp=drive_link

For additional information on the ADNI dataset, visit ADNI Website.