This project classifies brain MRI images into Alzheimer’s disease stages using a deep learning model based on ResNet18.
The goal is to explore how convolutional neural networks can assist in early detection of Alzheimer’s disease from MRI scans.
- Brain MRI images for Alzheimer’s disease classification
- Typical classes include:
- Non-Demented
- Very Mild Demented
- Mild Demented
- Moderate Demented
⚠️ Dataset is not included due to size and licensing constraints.
- Architecture: ResNet18
- Framework: PyTorch / TensorFlow (depending on implementation)
- Transfer learning with pretrained weights
- Final classification layer adapted for MRI classes
- Load and preprocess MRI images
- Apply data normalization and augmentation
- Train ResNet18-based classifier
- Evaluate performance using accuracy and confusion matrix
alzheimer_mri_resnet18.ipynb– Complete training and evaluation pipeline
📌 Start here: alzheimer_mri_resnet18.ipynb
The trained model demonstrates strong performance in distinguishing between Alzheimer’s disease stages based on MRI scans.
Gowtham