@inproceedings{314633c510a84d81b1f515da2ade72b1,
title = "Recent Developments in Artificial Intelligence-Based Techniques for Prostate Cancer Detection: A Scoping Review",
abstract = "Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories: diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that the implementation of AI-based tools will support clinicians to provide better diagnosis plans for prostate cancer.",
keywords = "Prostate cancer, deep learning, machine learning, medical imaging",
author = "Uzair Shah and Biswas, {Md Rafuil} and Alzubaidi, {Mahmood Saleh} and Hazrat Ali and Tanvir Alam and Mowafa Househ and Zubair Shah",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.",
year = "2022",
doi = "10.3233/SHTI210911",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "268--271",
editor = "John Mantas and Arie Hasman and Househ, {Mowafa S.} and Parisis Gallos and Emmanouil Zoulias and Joseph Liasko",
booktitle = "Informatics and Technology in Clinical Care and Public Health",
address = "Netherlands",
}