A State-of-the-Art Review on Machine Learning-based Methods for Prostate Cancer Diagnosis

Abstract

Prostate cancer can be viewed as the second most dangerous and diagnosed cancer of men all over the world. In the past decade, machine and deep learning methods play a significant role in improving the accuracy of classification for both binary and multi classifications. This review is aimed at providing a comprehensive survey of the state of the art in the past 5 years from 2015 to 2020, focusing on different datasets and machine learning techniques. Moreover, a comparison between studies and a discussion about the potential future researches is described. First, an investigation about the datasets used by the researchers and the number of samples associated with each patient is performed. Then, the accurate detection of each research study based on various machine learning methods is given. Finally, an evaluation of five techniques based on the receiver operating characteristic curve has been presented to show the accuracy of the best technique according to the area under curve (AUC) value. Conducted results indicate that the inception-v3 classifier has the highest score for AUC, which is 0.91.

Keywords

• prostate cancer
• machine learning
• deep learning
• algorithm
• datasets