Medical Image DataSet: Brain Tumor Detection

Citation Author(s):
Parisa
Karimi Darabi
Submitted by:
Zheng Linxuan
Last updated:
Sun, 03/09/2025 - 20:34
DOI:
10.21227/x3bv-p922
License:
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Abstract 

The Brain Tumor Detection Dataset is a dataset that's specifically designed for detecting brain tumours using advanced computer vision techniques. It's compatible with YOLOv8 an efficient and real-time object detection algorithm. The dataset was last updated about a year ago and is curated to help accurately detect and classify brain tumours into three distinct classes. The main goal of the project is to contribute to the early detection and diagnosis of brain tumours, which aims to provide valuable support to medical professionals in creating effective treatment plans. The dataset has a diverse range of brain tumour images that are carefully annotated to highlight the tumour regions corresponding to the specified labels. By using this dataset, researchers and practitioners can train and fine-tune computer vision models to achieve high accuracy in identifying and localizing brain tumours. The dataset's integration with YOLOv8 ensures real-time and efficient object detection, making it an ideal choice for applications that require timely and precise results. To see works done on it can visit its page on [Kaggle]( https://www.kaggle.com/datasets/pkdarabi/medical-image-dataset-brain-tum...).

Instructions: 

This dataset consists of 9,900 annotated brain MRI images, which are divided into a training set (6,930 images), a validation set (1,980 images), and a test set (990 images).The dataset includes annotations for three types of brain tumors:1abel 0: Glioma,1abel 1: Meningioma,1abel 2: Pituitary Tumor.