I need a medical dataset along with brest cancer by eeg for feature extraction. Could anyone suggest some publicly available datasets for download?

kaggle

There was a Data Science Bowl competition on Kaggle that has MRI scans for cancer detection. This was probably in 2019 or 2020. You can look it up.

It's great you're delving into medical image segmentation! Here's a breakdown of some prominent publicly available datasets, with considerations for your need for ground truth images:

Key Datasets and Resources:

• The Cancer Imaging Archive (TCIA):This is a very valuable resource. TCIA hosts a large collection of de-identified cancer-related medical images (CT, MRI, etc.) with associated clinical data. Crucially, many TCIA datasets include expert-generated segmentations, providing the ground truth you need. It's organized by cancer type, making it easier to find datasets relevant to your research.
• Medical Segmentation Decathlon:This is a benchmark dataset collection specifically designed for medical image segmentation. It covers a wide range of organs and pathologies, with datasets for:Brain tumors Liver tumors Heart structures And more. It provides well-defined challenges and ground truth annotations.
• Brain Tumor Segmentation (BraTS) Challenge:Specifically focused on brain tumor segmentation in MRI images. Provides multi-modal MRI data with detailed ground truth annotations for different tumor subregions. A very popular dataset for research in this area.
• NIH Chest X-ray Dataset:A massive dataset of chest X-ray images from the National Institutes of Health. While the primary focus is on classification, some related datasets and projects provide segmentation masks for lung structures and abnormalities.
• OpenNeuro:This repository is great for neuroimaging data. It has many MRI datasets, and other types of brain scans, that can be used for segmentation tasks.
• MedSegBench:This resource is made to give a comprehensive collection of medical images that are designed for segmentation. It is a very good benchmark dataset.

Important Considerations:

• Data Format:Medical images can be in various formats (DICOM, NIfTI, etc.). Ensure your software can handle the formats used in the dataset.
• Ground Truth Quality:The accuracy of ground truth annotations is crucial. Look for datasets with expert-generated segmentations.
• Ethical Use:Always adhere to the data usage agreements and ethical guidelines associated with each dataset. Patient privacy is paramount.

Where to Find Datasets:

• Many of these datasets are accessible through their respective websites or through platforms like:Kaggle: Often hosts medical imaging challenges and datasets. GitHub: Researchers frequently share datasets and code on GitHub.