Detecting Prostate Cancer Using A CNN-Based System Without Segmentation

Abstract

A computer-aided diagnosis (CAD) system for early detection of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) is proposed in this paper. The proposed system starts by defining a region of interest that includes the prostate across the different slices of the input DWI volume. Then, the apparent diffusion coefficient (ADC) of the defined ROI is calculated, normalized and refined. Finally, the classification of prostate into either benign or malignant is achieved using a classification system of two stages. In the first stage, seven convolutional neural networks (CNNs) are used to determine initial classification probabilities for each case. Then, an SVM with Guassian kernel is fed with these probabilities to determine the ultimate diagnosis. The proposed system is new in the sense that it has the ability to detect prostate cancer with minimal prior processing (e.g., rough definition of the prostate region). Evaluation of the developed system is done using DWI datasets collected at seven different b -values from 40 patients (20 benign and 20 malignant). The acquisition of these DWI datasets is performed using two different scanners with different magnetic field strengths (1.5 Tesla and 3 Tesla). The resulting area under curve (AUC) after the second stage of classification is 0.99, which shows a high performance of our system without segmentation similar to the performance of up-to-date systems.