Prostate Cancer Diagnosis Based on Cascaded Convolutional Neural Networks

Abstract

Interpreting magnetic resonance imaging (MRI) data by radiologists is time consuming and demands special expertise. Diagnosis of prostate cancer (PCa) with deep learning can also be time consuming and data storage consuming. This work presents an automated method for PCa detection based on cascaded convolutional neural network (CNN), including pre-network and post-network. The pre-network is based on a Faster-RCNN and trained with prostate images in order to separate the prostate from nearby tissues; the ResNet-based post-network is for PCa diagnosis, which is connected by bottlenecks and improved by applying batch normalization (BN) and global average pooling (GAP). The experimental results demonstrated that the cascaded CNN proposed had a good classification results on the in-house datasets, with less training time and computation resources.