Actively Recruiting
Renal Cancer Detection Using Convolutional Neural Networks
Led by Nessn Azawi · Updated on 2024-01-30
5000
Participants Needed
1
Research Sites
413 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. We would like to classify renal tumor to cancer, non cancer, renal cyst I, renal cyst II, renal cyst III and renal cyst VI, with high sensitivity and low false positive rate using various types of convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for renal cancer diagnosis. Moreover, by automating this task, we can significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans.
CONDITIONS
Official Title
Renal Cancer Detection Using Convolutional Neural Networks
Who Can Participate
Eligibility Criteria
You may qualify if you...
- All patients with renal cell carcinoma (RCC) who have undergone surgery
You will not qualify if you...
- Patients with renal cell carcinoma (RCC) who have not undergone surgery
AI-Screening
AI-Powered Screening
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Trial Site Locations
Total: 1 location
1
Zealand University Hospital
Roskilde, Denmark, 4000
Actively Recruiting
Research Team
N
Nessn Azawi, Phd
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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