CT Scan Image Datasets for Advanced AI/ML Applications in Healthcare
Accurate, De-Identified, and Scalable Data for Medical Diagnosis and AI/ML Model Training
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CT Scan Datasets: Accurate, De-Identified Data for Healthcare AI
Doctors rely on CT scan datasets to diagnose and detect abnormal or normal conditions in a patient’s body, such as identifying diseases or injuries in various body parts. During computerized image processing diagnosis, a CT scan image dataset undergoes sophisticated phases, including acquisition, image enhancement, extraction of critical features, Region of Interest (ROI) identification, and result interpretation. These datasets are vital for precise medical analysis and accurate diagnoses.
At Shaip, we offer high-quality CT scan image datasets essential for research and medical diagnosis. Our datasets include thousands of high-resolution images collected from real patients and processed with state-of-the-art techniques. These datasets cover a wide range of body parts, including the chest, brain, head, and knee, to help medical professionals and researchers better understand and address critical medical conditions such as cancer, neurological disorders, and cardiovascular diseases.
Our CT scan datasets are available in various formats, including DICOM, JPEG, and PNG, ensuring seamless integration into your AI/ML workflows. Whether you’re working on chest CT-Scan datasets for diagnosing lung diseases or brain CT datasets for neurological disorder analysis, Shaip provides reliable and accurate medical data to enhance your research and improve patient outcomes.
Body Part Central Asia Central Asia & Europe India Grand Total
Abdomen 500 350 850
Abdomen Contrast 100 100
Angio Lower Limb 100 100
Angio Pulmonary 100 100
Brain CT 100 100
C- Spine 350 350
Chest 6000 6000
CT Covid HRCT 100 100
Head 4000 350 4350
Hip 500 500
Knee 500 500
NSCLC 700 700
Pediatric Spine 350 350
Pelvis 500 350 850
RIB Fractured/WO 350 350
Thorax 350 350
Thorax Contrast 100 100
| Body Part | Central Asia | Central Asia & Europe | India | Grand Total |
|---|---|---|---|---|
| Abdomen | 500 | 350 | 850 | |
| Abdomen Contrast | 100 | 100 | ||
| Angio Lower Limb | 100 | 100 | ||
| Angio Pulmonary | 100 | 100 | ||
| Brain CT | 100 | 100 | ||
| C- Spine | 350 | 350 | ||
| Chest | 6000 | 6000 | ||
| CT Covid HRCT | 100 | 100 | ||
| Head | 4000 | 350 | 4350 | |
| Hip | 500 | 500 | ||
| Knee | 500 | 500 | ||
| NSCLC | 700 | 700 | ||
| Pediatric Spine | 350 | 350 | ||
| Pelvis | 500 | 350 | 850 | |
| RIB Fractured/WO | 350 | 350 | ||
| Thorax | 350 | 350 | ||
| Thorax Contrast | 100 | 100 |
We deal with all types of Data Licensing i.e., text, audio, video, or image. The datasets consist of Medical datasets for ML: Physician Dictation Dataset, Physician Clinical Notes, Medical Conversation Dataset, Medical Transcription Dataset, Doctor-Patient Conversation, Medical Text Data, Medical Images – CT Scan, MRI, Ultra Sound (collected basis custom requirements).
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Frequently Asked Questions (FAQ)
1. What are CT scan image datasets?
CT scan image datasets are collections of high-resolution images captured during CT scans, which are used to visualize internal body structures. These datasets are designed to support AI/ML research and medical diagnosis.
2. Why are CT scan image datasets important for AI/ML projects?
These datasets are essential for training AI models to automate diagnosis, detect abnormalities, and improve medical imaging analysis for conditions like cancer, neurological disorders, and cardiovascular diseases.
3. Which medical conditions can CT scan datasets help diagnose?
CT scan datasets assist in diagnosing a wide range of conditions, including cancer (e.g., lung cancer), brain disorders, cardiovascular diseases, fractures, and infections such as COVID-19.
4. What body parts are covered in the datasets?
The datasets include CT images of the brain, chest, abdomen, pelvis, spine, thorax, head, hips, knees, and more. Specialized datasets, like contrast-enhanced images, are also available.
5. What is the resolution of the CT scan images?
The datasets include high-resolution images that are suitable for precise medical analysis and AI/ML model training.
6. In what formats are the CT scan datasets available?
The datasets are provided in standard formats such as DICOM, PNG, or JPEG, ensuring compatibility with most AI/ML workflows.
7. Are the datasets de-identified?
Yes, all CT scan datasets are de-identified to remove personally identifiable information (PII), ensuring patient privacy and compliance with legal standards.
8. Do the datasets comply with HIPAA regulations?
Yes, the datasets are fully compliant with HIPAA and other global data privacy regulations to ensure secure and ethical usage.
9. Can the datasets be customized?
Yes, datasets can be tailored to specific needs, such as focusing on a particular body part, condition, or geographic region.
10. Are the datasets scalable for large AI projects?
Yes, the datasets are scalable and include thousands of images, making them suitable for both small-scale and large-scale AI/ML projects.
11. How can the datasets be integrated into AI workflows?
The datasets are delivered in standard formats with detailed metadata, making them easy to integrate into AI workflows for training, testing, and validation.
12. How is the quality of the datasets ensured?
The data undergoes rigorous quality checks, including expert annotation and validation, to ensure accuracy and reliability for AI training.
13. What is the cost of CT scan image datasets?
Costs depend on factors like dataset size, customization requirements, and project scope. We request that you fill out the “Contact Us” form with your requirements to receive the best quote.
14. What are the delivery timelines for the datasets?
Delivery timelines vary based on project size and complexity but are structured to meet your requirements efficiently.
15. How can CT scan datasets improve healthcare AI?
These datasets enable AI models to accurately detect and diagnose medical conditions, automate imaging workflows, and enhance patient care by providing reliable diagnostic insights.