Prostate Cancer Classification and Interpretation With Multiparametric Magnetic Resonance Imaging and Gleason Grade Score Using DarkNet53 Model
After comparing the proposed work with various state-of-the-art algorithms, the proposed model achieves 99.31% accuracy, 98.24% sensitivity, and 98.46% specificity, respectively, highlighting the model's accomplishment using the DarkNet53 classifier.
Authors
- V.P. Gladis Pushparathi
- Diego Ricardo Xavier (ORCID: https://orcid.org/0000-0001-5259-7732)
- G Kannan (ORCID: https://orcid.org/0000-0003-3020-4269)
- P. Chitra
Institutions
- Thiruvalluvar University (IN)
- Vels University (IN)
- St. Joseph's Institute of Technology (IN)
- Velammal Educational Trust (IN)
Publication Details
- Journal
- The Prostate
- Published
- 2024-11-25
- DOI
- https://doi.org/10.1002/pros.24827
- Citations
- 1
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.42