193K+ X-Ray Images Dataset (JPG)
Medical Imaging Data
Tags and Keywords
£143,500
About
193K+ X-Ray Images Dataset (JPG)
Description
The 193K+ X-Ray Images Dataset (JPG) is a large-scale medical imaging dataset developed for artificial intelligence, computer vision, radiology research, and healthcare analytics. The dataset contains over 193K+ high-quality X-ray images collected from 119K+ anonymized patients in JPEG (JPG) format, providing standardized imaging data for training, validating, and evaluating AI models. It supports a broad range of medical imaging applications, including image classification, object detection, anomaly detection, segmentation, and clinical imaging research. The JPEG format enables seamless integration into deep learning frameworks, computer vision pipelines, and healthcare AI workflows.
Note: Pricing varies depending on several factors, including the number of medical images, image quality and resolution, metadata availability, annotation requirements, imaging modality, file formats, and customization needs. The final price will be determined based on the specific dataset requirements.
Data Product Features
| Feature | Description |
|---|---|
| X-Ray Image | Digital X-ray image stored in JPEG (.jpg) format. |
| Modality | Imaging modality (X-Ray). |
| Body Region | Anatomical region imaged (Chest, Abdomen, Spine, Hand, Foot, Pelvis, etc.). |
| Image Resolution | Width and height of the image in pixels. |
| File Format | JPEG (.jpg). |
Distribution
- Dataset Format: JPEG (.jpg)
Data Volume
- 193K+ X-Ray Images
- 119K+ anonymized patients
- Multiple imaging studies across various anatomical regions
- High-resolution JPEG images
- Single imaging modality (X-Ray)
- Dataset Size: The dataset size may vary depending on image resolution, image dimensions, compression quality, metadata availability, annotations, and dataset version.
Usage
This data product is ideal for a variety of applications:
- Medical Image Classification: Train AI models to classify X-ray images.
- Disease Detection: Develop algorithms for detecting abnormalities in radiographic images.
- Medical Image Segmentation: Build AI models for bone, organ, and tissue segmentation.
- Object Detection: Detect fractures, implants, and anatomical structures.
- Anomaly Detection: Identify abnormal imaging patterns.
- Computer Vision: Train deep learning models for medical image understanding.
- Foundation Model Training: Develop vision foundation models for healthcare AI.
- Academic Research: Support research in radiology, computer vision, and medical image analysis.
Coverage
- Geographic Coverage: Global
License
CC BY 4.0 (Creative Commons Attribution 4.0 International)
AI Training Rights
InfoBay.AI ensures that all datasets are sourced, curated, and managed with proper ownership verification, licensing documentation, and data provenance records. We hold the necessary rights to license and sublicense the datasets we provide through formal agreements with our data vendors, which grant us the required permissions for commercial licensing and AI training use cases. To ensure transparency and compliance, we maintain relevant documentation and have previously shared redacted agreements for selected datasets as evidence of our data rights and licensing authority.
Data Dictionary
| Column Name | Data Type | Description | Possible Values/Notes |
|---|---|---|---|
| Modality | String | Imaging modality | X-Ray |
| Body_Region | String | Anatomical region imaged | Chest, Abdomen, Spine, Pelvis, Hand, Foot, etc. |
| Image_File | String | X-ray image file | .jpg |
| Image_Width | Integer | Image width in pixels | Positive integer |
| Image_Height | Integer | Image height in pixels | Positive integer |
| File_Format | String | Image file format | JPG |
Considerations
This dataset is provided for research and educational purposes only. It contains only sample data.
Additional Notes
- This dataset contains X-ray images only and does not include radiology findings.
- Suitable for image classification, segmentation, object detection, anomaly detection, self-supervised learning, transfer learning, medical image preprocessing, and healthcare AI research.
- Optimized for large-scale computer vision, medical imaging, and radiology AI workflows.
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£143,500
Download Dataset in IMAGE Format
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