Brain Tumor Image DataSet : Semantic Segmentation

Brain Tumor Image DataSet : Semantic Segmentation

Datasets

Brain Tumor Image DataSet : Semantic Segmentation

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Brain Tumor Image DataSet : Semantic Segmentation

Use Case

Computer Vision

Description

The Tumor Segmentation Dataset is designed specifically for the TumorSeg Computer Vision Project, which focuses on Semantic Segmentation.

About Dataset

Brain Tumor Image DataSet: Semantic Segmentation

The Tumor Segmentation Dataset is designed specifically for the TumorSeg Computer Vision Project, which focuses on Semantic Segmentation. The project aims to identify tumor regions accurately within Medical Images using advanced techniques.

Details of the Dataset:

Project Type: Semantic Segmentation

Subject: Tumor

Classes:

  • Tumor (Class 1)
  • Non-Tumor (Class 0)

Key Information:

  • Last Updated: 9 months ago

Overview of the Project:

The TumorSeg Computer Vision Project is dedicated to Semantic Segmentation, which involves classifying every pixel in an image as part of a tumor or non-tumor region. This fine-grained approach provides an accurate understanding of the spatial distribution of tumors within medical images.

Classes:

This dataset is divided into two classes:
Class 0 (Non-Tumor): Pixels labeled as non-tumor represent areas without any tumor presence in the medical images.
Class 1 (Tumor): Pixels labeled as tumors correspond to areas where tumors are detected.

Usage of the Dataset:

Researchers and practitioners in the field of medical image analysis can use this dataset to train and evaluate semantic segmentation models for tumor detection. The binary classification into tumor and non-tumor classes simplifies the segmentation task, making it suitable for various applications.

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