Olabanjo, O.; Wusu, A.; Asokere, M.; Afisi, O.; Okugbesan, B.; Olabanjo, O.; Folorunso, O.; Mazzara, M. Application of Machine Learning and Deep Learning Models in Prostate Cancer Diagnosis Using Medical Images: A Systematic Review. Analytics2023, 2, 708-745.
Olabanjo, O.; Wusu, A.; Asokere, M.; Afisi, O.; Okugbesan, B.; Olabanjo, O.; Folorunso, O.; Mazzara, M. Application of Machine Learning and Deep Learning Models in Prostate Cancer Diagnosis Using Medical Images: A Systematic Review. Analytics 2023, 2, 708-745.
Olabanjo, O.; Wusu, A.; Asokere, M.; Afisi, O.; Okugbesan, B.; Olabanjo, O.; Folorunso, O.; Mazzara, M. Application of Machine Learning and Deep Learning Models in Prostate Cancer Diagnosis Using Medical Images: A Systematic Review. Analytics2023, 2, 708-745.
Olabanjo, O.; Wusu, A.; Asokere, M.; Afisi, O.; Okugbesan, B.; Olabanjo, O.; Folorunso, O.; Mazzara, M. Application of Machine Learning and Deep Learning Models in Prostate Cancer Diagnosis Using Medical Images: A Systematic Review. Analytics 2023, 2, 708-745.
Abstract
Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, more specifically with higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. The recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there is a growth in recent related publications.
Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review through relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified and 77 met the final inclusion criteria. Key elements in the included papers were presented and conclusions were drawn from them.
Results: Findings showed that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for choice of loss function in the deep learning models were presented. The limitations and lessons learnt were discussed and some key recommendations were made.
Conclusion: The discoveries and the conclusions of this work have been organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.
Keywords
machine learning; deep learning; prostate cancer; review; PRISMA; CNN
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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