Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging
Article
Article Title | Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging |
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ERA Journal ID | 4403 |
Article Category | Article |
Authors | Yildiri, Kadir, Yildirim, Muhammed, Eryesil, Hasan, Talo, Muhammed, Yildirim, Ozal, Karabatak, Murat, Ogras, Mehmet Sezai, Artas, Hakan and Acharya, U Rajendra |
Journal Title | Computers and Electrical Engineering |
Journal Citation | 102 |
Article Number | 108275 |
Number of Pages | 11 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0045-7906 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compeleceng.2022.108275 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0045790622005043 |
Abstract | Prostate cancer (PCa) is the most common type of cancer among men. Digital rectal examination and prostate-specific antigen (PSA) tests are used to diagnose the PCa accurately. Since PSA is organ-specific and not disease-specific, multiparametric magnetic resonance imaging (mpMRI) is used to reduce unnecessary biopsies. Prostate imaging reporting and data system (PI-RADS) is widely used for mpMRI scoring to detect PCa. There is low-level agreement among interpreters and also subjectivity associated with PI-RADS scoring. Hence, in this study, a hybrid model has been proposed to accurately interpret mpMRI examination and predict PI-RADS scores. In the proposed systems, feature maps of mpMR images were extracted using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. Then, the feature maps obtained using these three architectures were combined. The merged feature maps are subjected to neighborhood components analysis (NCA) to eliminate redundant features. The proposed system provided 96.09% accuracy. |
ANZSRC Field of Research 2020 | 400303. Biomechanical engineering |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Health Sciences, Turkey |
Malatya Turgut Ozal University, Turkiye | |
Firat University, Turkey | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
University of Southern Queensland |
https://research.usq.edu.au/item/z02z5/deep-learning-based-pi-rads-score-estimation-to-detect-prostate-cancer-using-multiparametric-magnetic-resonance-imaging
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