Using CNN With Handcrafted Features for Prostate Cancer Classification

Abstract

Prostate cancer has been a leading cause of death among males for a long time. Currently, with the help of computer-aided detection systems, prostate cancer can be detected in a relatively early stage, thus improving the patients' survival rate. In this paper, we propose a computer-aided system based on deep learning method to help classify prostate cancer. Our model combines both convolutional neural network (CNN) extracted features and handcrafted features. In our model, the input data is sent into two subnets. One is a modified ResNet with an improved spatial transformer (ST) for high dimension feature extraction. The other subnet extracts three handcrafted features and processes them with a simple CNN. After those two subnets, the output features of the two subnets are concatenated and then sent into the final classifier for prostate cancer classification. Experimental results show that our model achieves an accuracy of 0.947, which is better than other state-of-the-art methods.