DDV: A Taxonomy for Deep Learning Methods in Detecting Prostate Cancer

Abstract

Deep learning is increasingly studied in the prediction of cancer yet few deep learning systems have been introduced for daily use for such purpose. The manual scanning, reading, and analysis by radiologists to detect cancer are very time-consuming processes due to their large volume. Although many types of research have been conducted in this area, the use of their results in the diagnosis of prostate cancer is yet to be properly carried out. In this paper, a Data, Detection, and View (DDV) taxonomy is introduced that defines each major component, which is required to implement a proper deep learning prostate cancer detection system. The proposed taxonomy is a step toward developing a way to assist the pathologists for early detecting prostate cancer and hence facilitating the patients to seek speedy counseling from the doctors. If the diagnosis of cancer can be performed in the early stages then it can be prevented from spreading to other cells. The components of the proposed taxonomy must be reviewed and used as an evaluation and validation criteria for approving the deep learning classification model to be used in real-world clinical practices. Through the study of 22 state-of-the-art research papers in the field of deep learning-based prostate cancer classification system, we proved the effectiveness and robustness of the proposed DDV taxonomy system.