Link to this sectionBrain Tumor Dataset#

The Ultralytics Brain Tumor dataset is an object detection dataset of 1,116 medical images (893 for training and 223 for validation) from MRI and CT scans, labeled across 2 classes: negative (no tumor) and positive (tumor present). It lets you train computer vision models to locate brain tumors in scans, supporting early diagnosis and treatment planning in healthcare applications.

Link to this sectionDataset Structure#

The brain tumor dataset contains 1,116 images split into two predefined subsets, defined by the brain-tumor.yaml configuration:

Every image is labeled with one of 2 classes:

• negative: images without a brain tumor
• positive: images showing a brain tumor

The dataset downloads automatically (4.21 MB) from Ultralytics GitHub assets the first time you train, so no manual setup is required.

Explore Brain Tumor on Ultralytics Platform to browse the images with their annotation overlays, view the class distribution and bounding-box heatmaps in the Charts tab, and clone it to train your own model in the cloud.

Link to this sectionApplications#

Brain tumor detection with computer vision enables early diagnosis, treatment planning, and tumor-progression monitoring. By analyzing MRI or CT scans, detection models accurately locate tumors, supporting timely medical intervention and personalized treatment.

Medical professionals can leverage this technology to:

• Reduce diagnostic time and improve accuracy
• Assist in surgical planning by precisely locating tumors
• Monitor treatment effectiveness over time
• Support research in oncology and neurology

Link to this sectionDataset YAML#

A YAML file defines the dataset configuration, including paths, classes, and other relevant information. For the brain tumor dataset, the brain-tumor.yaml file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/brain-tumor.yaml.

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# Brain-tumor dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/detect/brain-tumor
# Example usage: yolo train data=brain-tumor.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── brain-tumor ← downloads here (4.21 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: brain-tumor # dataset root dir
train: images/train # train images (relative to 'path') 893 images
val: images/val # val images (relative to 'path') 223 images

# Classes
names:
  0: negative
  1: positive

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/brain-tumor.zip

Link to this sectionUsage#

To train a YOLO26 model on the brain tumor dataset for 100 epochs with an image size of 640, utilize the provided code snippets. For a detailed list of available arguments, consult the model's Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)

from ultralytics import YOLO

# Load a model
model = YOLO("path/to/best.pt")  # load a brain-tumor fine-tuned model

# Inference using the model
results = model.predict("https://ultralytics.com/assets/brain-tumor-sample.jpg")

Link to this sectionSample Images and Annotations#

The brain tumor dataset contains MRI and CT brain scans with and without tumors. Below is an example image from the dataset with its annotations.

• Mosaiced Image: This training batch shows mosaiced dataset images. Mosaicing combines multiple images into one during training, increasing batch diversity so the model generalizes better across tumor sizes, shapes, and locations for medical image analysis.

Link to this sectionCitations and Acknowledgments#

The dataset has been made available under the AGPL-3.0 License.

If you use this dataset in your research or development work, please cite it appropriately:

@dataset{Ultralytics_Brain_Tumor_Dataset_2023,
    author = {Ultralytics},
    title = {Brain Tumor Detection Dataset},
    year = {2023},
    publisher = {Ultralytics},
    url = {https://docs.ultralytics.com/datasets/detect/brain-tumor/}
}

Link to this sectionFAQ#

Link to this sectionWhat is the structure of the brain tumor dataset available in Ultralytics documentation?#

The brain tumor dataset contains 1,116 images divided into two subsets: a training set of 893 images and a validation set of 223 images, each with paired annotations. This structured division supports developing robust and accurate computer vision models for detecting brain tumors. For more information, see the Dataset Structure section.

Link to this sectionWhat classes does the brain tumor dataset contain?#

The brain tumor dataset has 2 classes: negative (images without a brain tumor) and positive (images showing a brain tumor). This binary labeling lets a detection model both locate a tumor and flag scans where none is present.

Link to this sectionHow do I download the brain tumor dataset?#

The brain tumor dataset (4.21 MB) downloads automatically from Ultralytics GitHub assets the first time you train with data="brain-tumor.yaml" — no manual download is required. You can browse related datasets in the detection datasets overview.

Link to this sectionHow can I train a YOLO26 model on the brain tumor dataset using Ultralytics?#

You can train a YOLO26 model on the brain tumor dataset for 100 epochs with an image size of 640px using both Python and CLI methods. Below are the examples for both:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)

For a detailed list of available arguments, refer to the Training page.

Link to this sectionWhat are the benefits of using the brain tumor dataset for AI in healthcare?#

Using the brain tumor dataset in AI projects enables early diagnosis and treatment planning for brain tumors. It helps in automating brain tumor identification through computer vision, facilitating accurate and timely medical interventions, and supporting personalized treatment strategies. This application holds significant potential in improving patient outcomes and medical efficiencies. For more insights on AI applications in healthcare, see Ultralytics' healthcare solutions.

Link to this sectionHow do I perform inference using a fine-tuned YOLO26 model on the brain tumor dataset?#

Inference using a fine-tuned YOLO26 model can be performed with either Python or CLI approaches. Here are the examples:

from ultralytics import YOLO

# Load a model
model = YOLO("path/to/best.pt")  # load a brain-tumor fine-tuned model

# Inference using the model
results = model.predict("https://ultralytics.com/assets/brain-tumor-sample.jpg")

Link to this sectionWhere can I find the YAML configuration for the brain tumor dataset?#

The YAML configuration file for the brain tumor dataset can be found at brain-tumor.yaml. This file includes paths, classes, and additional relevant information necessary for training and evaluating models on this dataset.