task_categories:image-classification size_categories:1K<n<10K language:en license:apache-2.0 medical region:us
---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': Mild_Demented
          '1': Moderate_Demented
          '2': Non_Demented
          '3': Very_Mild_Demented
  splits:
  - name: train
    num_bytes: 22560791.2
    num_examples: 5120
  - name: test
    num_bytes: 5637447.08
    num_examples: 1280
  download_size: 28289848
  dataset_size: 28198238.28
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- medical
pretty_name: Alzheimer_MRI Disease Classification Dataset
size_categories:
- 1K<n<10K
---
# 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:

```python
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)
```