# Prostate Cancer Detection using Deep Convolutional Neural Networks

## TL;DR

This paper presents a CNN-based automated pipeline for detecting clinically significant prostate cancer using diffusion-weighted MRI images, achieving high accuracy in a dataset of 427 patients.

## Contribution

The study develops and validates a novel CNN pipeline specifically designed for prostate cancer detection from DWI images, demonstrating promising diagnostic performance.

## Key findings

- Achieved AUC of 0.87 at slice level
- Achieved AUC of 0.84 at patient level
- Validated on a dataset of 427 patients

## Abstract

Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNNs architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNNs-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 healthy patients. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95% Confidence Interval (CI): 0.84-0.90) and 0.84 (95% CI: 0.76-0.91) at slice level and patient level, respectively.