MedTrace Logo

Renal Cancer Detection Using Convolutional Neural Networks

NCT03857373 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 5000

Last updated 2024-01-30

No results posted yet for this study

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.

Conditions

Sponsors & Collaborators

  • Nessn Azawi

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-02-01
Primary Completion
2025-01-01
Completion
2027-01-01

Countries

  • Denmark

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03857373 on ClinicalTrials.gov