Brain3-Anomaly-Classification

The Brain3-Anomaly-Classification dataset is a curated collection of brain MRI scans categorized into three types of brain anomalies. It is designed for use in machine learning applications related to medical imaging, especially in the detection and classification of brain tumors.

Dataset Summary

• Total Samples: 6,000
• Image Size: 512 x 512 pixels (grayscale)
• Number of Classes: 3
• Data Split: Only train split is provided

Each image in the dataset represents an MRI scan and is annotated with a label indicating the type of brain anomaly.

Label Mapping

Label	Class Name
0	brain_glioma
1	brain_menin
2	brain_tumor

Use Cases

This dataset can be used for:

• Classification of brain anomalies using deep learning
• Training and evaluation of CNNs on medical image data
• Benchmarking models for medical diagnosis
• Research on MRI-based brain disease detection

Dataset Structure

Each row in the dataset includes:

• image: A brain MRI image of size 512x512 pixels
• label: An integer representing the class (0, 1, or 2)

The dataset is available in Hugging Face's Parquet format for easy loading and use with the datasets library.

How to Use

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Brain3-Anomaly-Classification")

This will load the dataset with images and corresponding labels, which can be directly used for training models in PyTorch, TensorFlow, or other ML frameworks.