Prostate cancer diagnosis with deep learning
Bangqi Wang · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · 2017
Prostate cancer is one of the most common cancers and the second leading cause of death among American men. However, prostate cancer diagnosis is one of the most urgent problems confronted by scientific research. Accurate prostate cancer diagnosis needs a great degree of medical knowledge and is usually based on experience. It is hard for ordinary men to diagnose prostate cancer by themselves. This project aims to eliminate the knowledge barrier and provide a precise and effective method using deep learning. This project uses a deep learning neural network to build a binary classifier for prostate needle biopsies from patients. The project enlarges the prostate cancer needle biopsies dataset using randomly cutting, builds the deep learning network binary classifier, and generates predictions for the biopsies. The classifier will assign a benign or malignant label to every biopsy with accuracy near 100%.