Data Source

https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection

  Dataset Card Authors

Mahadi Hassan

  Dataset Card Contact





  mahadise01@gmail.com





  Linkdin: https://www.linkedin.com/in/mahadise01





  Github: https://github.com/Mahadih534

Brain Tumor Kaggle

## Overview

Brain Tumor Kaggle is a dataset for object detection tasks - it contains Cancer Mfjw annotations for 4,104 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset is collected from Kaggle ( https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset ). This dataset is a combination of the following three datasets :figshareSARTAJ datasetBr35H

The MCND dataset incorporates MRI data from three neurological disorders, released on the Kaggle repository. These include Alzheimer’s Disease (AD) [1], Brain Tumor (BT) [2], and Multiple Sclerosis (MS) [3]. This dataset contains 16400 images of human brain MRI images which are classified into 8 classes: AD-MildDemented, AD-ModerateDemented, AD-VeryMildDemented, BT-glioma, BT-meningioma, BT-pituitary, MS, and Normal (healthy).

1. https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images
2. https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset/data
3. https://www.kaggle.com/datasets/buraktaci/multiple-sclerosis

Abstract

A dataset of training and test images for the Brain Tumor Identification notebook found at: https://www.kaggle.com/code/faridtaghiyev/brain-tumor-detection-using-tensorflow-2-x/notebook

Methodology

The dataset comprises MRI images labeled for brain tumor presence. Images are split into training (70%), validation (15%), and test (15%) sets. Preprocessing includes resizing to 256x256 pixels, normalization, and augmentation (rotation, flipping). Models are trained using TensorFlow on a CNN architecture, optimized with Adam, and evaluated based on accuracy, precision, recall, and F1-score.

Usage

This public dataset is available for non-commercial use. Any publications or derivatives from these data must credit the original source. Please cite appropriately when using or referencing this dataset in any capacity.

Kaggle Brain Tumor Add

## Overview

Kaggle Brain Tumor Add is a dataset for object detection tasks - it contains Brain Add annotations for 3,906 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This Brain Tumor Prediction Dataset contains 250,000 patient records with 22 important medical features. The data includes MRI scan results, tumor size, genetic risks, symptoms, lifestyle habits, and treatment details. It is designed for predictive modeling, data analysis, and AI applications in healthcare.

✅ Why Use This Dataset?

Large-scale realistic medical data (250K rows) Includes tumor location, growth rate, and survival rate Useful for machine learning, deep learning, and medical research Perfect for classification and survival analysis Supports global health insights (data from multiple countries)

Dataset description

This dataset is a combination of the following three datasets : FigshareSARTAJ datasetBr35H This dataset contains 7023 images of human brain MRI images which are divided into 4 classes: glioma - meningioma - no tumor and pituitary. No tumor class images were taken from the Br35H dataset.

  Acknowledgement

This dataset is reproduced and taken from Kaggle

This is the data set used in the paper Brain Tumor Classification (MRI), and the complete data set can be found at: https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri

Gazi Brains 2020 dataset

This is a Brain MRI dataset from the Turkish Brain Project

This dataset contains 100 scans of 100 subjects, 50 of those scans belong to patients with histopathologically diagnosed High-Grade Glial (HGG) tumors, and 50 belong to normal healthy subjects.

Subjects 1-50 are the HGG group and subjects 51-100 are the normal group.

All scans have FLAIR, T1w, and T2w sequences, all HGG scans include a Gadolinium-enhanced T1w sequence, and 12 normal group scans have contrast-enhanced series.

All sequences of all scans are registered to match their FLAIR sequence. Then defaced using manually drawn masks to protect subject privacy but avoid losing region of interest (ROI) structures such as eye and orbita.

When drawing deface masks nose, cheeks, and teeth (if included in the scan) are considered identifyible structures and deleted from scans (intensity = zero) with a margin to prevent reconstructing from the gap left.

MRI quality metrics are obtained using MRIQC software.

✔️Abstract A Brain tumor is considered as one of the aggressive diseases, among children and adults. Brain tumors account for 85 to 90 percent of all primary Central Nervous System (CNS) tumors. Every year, around 11,700 people are diagnosed with a brain tumor. The 5-year survival rate for people with a cancerous brain or CNS tumor is approximately 34 percent for men and36 percent for women. Brain Tumors are classified as: Benign Tumor, Malignant Tumor, Pituitary Tumor, etc. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties. Application of automated classification techniques using Machine Learning (ML) and Artificial Intelligence (AI) has consistently shown higher accuracy than manual classification. Hence, proposing a system performing detection and classification by using Deep Learning Algorithms using Convolution-Neural Network (CNN), Artificial Neural Network (ANN), and Transfer-Learning (TL) would be helpful to doctors all around the world.

✔️ Context Brain Tumors are complex. There are a lot of abnormalities in the sizes and location of the brain tumor(s). This makes it really difficult for complete understanding of the nature of the tumor. Also, a professional Neurosurgeon is required for MRI analysis. Often times in developing countries the lack of skillful doctors and lack of knowledge about tumors makes it really challenging and time-consuming to generate reports from MRI’. So an automated system on Cloud can solve this problem.

✔️ Definition To Detect and Classify Brain Tumor using, CNN and TL; as an asset of Deep Learning and to examine the tumor position(segmentation).

✔️ About the data: The dataset contains 3 folders: yes, no and pred which contains 3060 Brain MRI Images.

Folder Description Yes The folder yes contains 1500 Brain MRI Images that are tumorous No The folder no contains 1500 Brain MRI Images that are non-tumorous By: Ahmed Hamada

This dataset contains a collection of multimodal medical images, specifically CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) scans, for brain tumor detection and analysis. It is designed to assist researchers and healthcare professionals in developing AI models for the automatic detection, classification, and segmentation of brain tumors. The dataset features images from both modalities, providing comprehensive insight into the structural and functional variations in the brain associated with various types of tumors.

The dataset includes high-resolution CT and MRI images captured from multiple patients, with each image labeled with the corresponding tumor type (e.g., glioma, meningioma, etc.) and its location within the brain. This combination of CT and MRI images aims to leverage the strengths of both imaging techniques: CT scans for clear bone structure visualization and MRI for soft tissue details, enabling a more accurate analysis of brain tumors.

I collected these data from different sources and modified data for maximum accuracy.

Brain Tumor CT scan Images source

1. CT Brain Segmentation Computer Vision Project -- https://universe.roboflow.com/joshua-zgc7b/ct-brain-segmentation
2. CT and MRI brain scans -- https://www.kaggle.com/datasets/darren2020/ct-to-mri-cgan
3. CT Head Scans(jpg files) -- https://www.kaggle.com/datasets/clarksaben/ct-head-scans
4. Head CT Images for Classification -- https://www.kaggle.com/datasets/nipaanjum/head-ct-images-for-classification
5. Anonymous brain -- from private data
6. Unpaired MR-CT Brain Dataset for Unsupervised Image Translation -- https://data.mendeley.com/datasets/z4wc364g79/1

Brain Tumor MRI images source

1. Brain Tumor (MRI Scans) -- https://www.kaggle.com/datasets/rm1000/brain-tumor-mri-scans
2. Brain Tumor MRIs -- https://www.kaggle.com/datasets/vinayjayanti/brain-tumor-mris
3. Siardataset -- https://www.kaggle.com/datasets/masoumehsiar/siardataset
4. Brain tumors 256x256 -- https://www.kaggle.com/datasets/thomasdubail/brain-tumors-256x256
5. Brain Tumor MRI Image Classification Dataset -- https://www.kaggle.com/datasets/iashiqul/brain-tumor-mri-image-classification-dataset
6. Brain Tumor MRI (yes or no) -- https://www.kaggle.com/datasets/mohamada2274/brain-tumor-mri-yes-or-no
7. BRAIN TUMOR CLASS CLASS Computer Vision Project -- https://universe.roboflow.com/college-sf5ih/brain-tumor-class-class
8. Brain Tumor Detection Computer Vision Project -- https://universe.roboflow.com/tuan-nur-afrina-zahira/brain-tumor-detection-bmmqz
9. Tumor Detection Computer Vision Project -- https://universe.roboflow.com/brain-tumor-detection-wsera/tumor-detection-ko5jp

Dataset

This dataset was created by Fahim Yusuf

Released under Data files © Original Authors

Contents

Analysis of ‘Brain Tumor’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jillanisofttech/brain-tumor on 28 January 2022.

--- Dataset description provided by original source is as follows ---

What is a brain tumor?

A brain tumor is a collection, or mass, of abnormal cells in your brain. Your skull, which encloses your brain, is very rigid. Any growth inside such a restricted space can cause problems. Brain tumors can be cancerous (malignant) or noncancerous (benign). When benign or malignant tumors grow, they can cause the pressure inside your skull to increase. This can cause brain damage, and it can be life-threatening.

The importance of the subject

Early detection and classification of brain tumors is an important research domain in the field of medical imaging and accordingly helps in selecting the most convenient treatment method to save patients life therefore

About The Dataset

This Brain Tumor Dataset Contain 7465 columns and 1 dependent or target Column. Total Column 7466. It's a Classification Problem.

--- Original source retains full ownership of the source dataset ---

Dataset Card for "brain-tumor-image-dataset-semantic-segmentation"

  Dataset Description

The Brain Tumor Image Dataset (BTID) for Semantic Segmentation contains MRI images and annotations aimed at training and evaluating segmentation models. This dataset was sourced from Kaggle and includes detailed segmentation masks indicating the presence and boundaries of brain tumors. This dataset can be used for developing and benchmarking algorithms for medical image segmentation… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/brain-tumor-image-dataset-semantic-segmentation.

The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan. The dataset also provides full masks for brain tumors, with labels for ED, ET, NET/NCR. The segmentation evaluation is based on three tasks: WT, TC and ET segmentation.

malignant

Dataset

This dataset was created by Ashiq

Contents

meningiomas

The BraTS 2015 dataset is a dataset for brain tumor image segmentation. It consists of 220 high grade gliomas (HGG) and 54 low grade gliomas (LGG) MRIs. The four MRI modalities are T1, T1c, T2, and T2FLAIR. Segmented “ground truth” is provide about four intra-tumoral classes, viz. edema, enhancing tumor, non-enhancing tumor, and necrosis.