Alzheimer_MRI Disease Classification Dataset

The Falah/Alzheimer_MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories:

• '0': Mild_Demented
• '1': Moderate_Demented
• '2': Non_Demented
• '3': Very_Mild_Demented

Dataset Information

• Train split:

  • Name: train
  • Number of bytes: 22,560,791.2
  • Number of examples: 5,120

• Test split:

  • Name: test
  • Number of bytes: 5,637,447.08
  • Number of examples: 1,280

• Download size: 28,289,848 bytes

• Dataset size: 28,198,238.28 bytes

Citation

If you use this dataset in your research or health medicine applications, we kindly request that you cite the following publication:

@dataset{alzheimer_mri_dataset,
  author = {Falah.G.Salieh},
  title = {Alzheimer MRI Dataset},
  year = {2023},
  publisher = {Hugging Face},
  version = {1.0},
  url = {https://huggingface.co/datasets/Falah/Alzheimer_MRI}
}

Usage Example

Here's an example of how to load the dataset using the Hugging Face library:

from datasets import load_dataset

# Load the Falah/Alzheimer_MRI dataset
dataset = load_dataset('Falah/Alzheimer_MRI', split='train')

# Print the number of examples and the first few samples
print("Number of examples:", len(dataset))
print("Sample data:")
for example in dataset[:5]:
    print(example)