The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project.

The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project. The investigators want to classify bladder tumors as cancer, non cancer, high grade and low grade, invasive and non-invasive, with high sensitivity and low false positive rate using various convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for bladder cancer diagnosis. Moreover, by automating this task, the investigator scan significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans and reduce the false-negative and positive that can happen due to human evaluation cystoscopies.

Inclusion Criteria:

• Patients with first time hematuria
• Patients with the control program for previous bladder cancer

Exclusion Criteria:

• Patients with control cystoscope for noncancer suspected disease

Patients with micro or macroscopic hematuria