Renal Cancer Detection Using Convolutional Neural Networks
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Kidney Cancer
- Design
- Observational Model: CohortTime Perspective: Prospective
Participation Requirements
- Age
- Younger than 125 years
- Gender
- Both males and females
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 canc...
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.
Tracking Information
- NCT #
- NCT03857373
- Collaborators
- Not Provided
- Investigators
- Not Provided