Dataset Labels

['tumor']

Number of Images

{'valid': 234, 'test': 125, 'train': 2377}

How to Use

Install datasets:

pip install datasets

Load the dataset:

from datasets import load_dataset

ds = load_dataset("disha07/brainybrain", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9/dataset/1

Citation

@misc{
                            brain-lesion-kmiz9_dataset,
                            title = { Brain lesion  Dataset },
                            type = { Open Source Dataset },
                            author = { brain leisons },
                            howpublished = { \\url{ https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9 } },
                            url = { https://universe.roboflow.com/brain-leisons/brain-lesion-kmiz9 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-04-21 },
                            }

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on April 20, 2024 at 11:41 AM GMT

Roboflow is an end-to-end computer vision platform that helps you

collaborate with your team on computer vision projects
collect & organize images
understand and search unstructured image data
annotate, and create datasets
export, train, and deploy computer vision models
use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 2736 images. Lesion are annotated in COCO format.

The following pre-processing was applied to each image:

Auto-orientation of pixel data (with EXIF-orientation stripping)
Resize to 640x640 (Stretch)

The following augmentation was applied to create 3 versions of each source image:

The following transformations were applied to the bounding boxes of each image:

50% probability of horizontal flip