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.

深度学习在癌症预测中的研究越来越多，但为此目的每天引入的深度学习系统很少。放射科医生手工扫描，阅读和分析以检测癌症，由于其体积庞大，因此非常耗时。尽管在该领域已经进行了许多类型的研究，但是将它们的结果用于前列腺癌的诊断尚待适当地进行。本文介绍了一种数据，检测和查看（DDV）分类法，该分类法定义了每个主要组成部分，而这是实现适当的深度学习前列腺癌检测系统所必需的。拟议的分类法是朝着开发一种方法来帮助病理学家及早发现前列腺癌迈出的一步，从而便利了患者寻求医生的快速咨询。如果可以在早期进行癌症诊断，则可以防止其扩散到其他细胞。提议的分类法的组成部分必须进行审核，并用作评估和验证标准，以批准在现实世界中的临床实践中使用的深度学习分类模型。通过在基于深度学习的前列腺癌分类系统领域中的22篇最新研究论文的研究，我们证明了所提出的DDV分类系统的有效性和鲁棒性。