A Comparative Assessment of Feed-Forward and Convolutional Neural Networks for the Classification of Prostate Lesions

Abstract

Prostate cancer is the most common cancer in men in the UK. An accurate diagnosis at the earliest stage possible is critical in its treatment. Multi-parametric Magnetic Resonance Imaging is gaining popularity in prostate cancer diagnosis, it can be used to actively monitor low-risk patients, and it is convenient due to its non-invasive nature. However, it requires specialist knowledge to review the abundance of available data, which has motivated the use of machine learning techniques to speed up the analysis of these many and complex images. This paper focuses on assessing the capabilities of two neural network approaches to accurately discriminate between three tissue types: significant prostate cancer lesions, non-significant lesions, and healthy tissue. For this, we used data from a previous SPIE ProstateX challenge that included significant and non-significant lesions, and we extended the dataset to include healthy prostate tissue due to clinical interest. Feed-Forward and Convolutional Neural Networks have been used, and their performances were evaluated using 80/20 training/test splits. Several combinations of the data were tested under different conditions and summarised results are presented. Using all available imaging data, a Convolutional Neural Network three-class classifier comparing prostate lesions and healthy tissue attains an Area Under the Curve of 0.892.