Ignite Creation Date:
2024-05-06 @ 12:48 PM
Last Modification Date:
2024-10-26 @ 1:04 PM
Study NCT ID:
NCT03857373
Status:
RECRUITING
Last Update Posted:
2024-01-30
First Post:
2019-02-26
Brief Title:
Renal Cancer Detection Using Convolutional Neural Networks
Sponsor:
Nessn Azawi
Organization:
Zealand University Hospital
Study Overview
Official Title:
Renal Cancer Detection Using Convolutional Neural Networks
Status:
RECRUITING
Status Verified Date:
2024-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
RCCCNN
Brief Summary:
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
Detailed Description:
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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None