Convolutional neural networks for prostate cancer detection, classification, and segmentation: A systematic review and bibliometric analysis

Background: Prostate cancer represents the second most common malignancy among men globally, necessitating accurate diagnostic methodologies for optimal patient outcomes. Convolutional neural networks (CNNs), a core deep learning methodology, have emerged as transformative technologies for automated prostate cancer detection, classification, and segmentation across multiple imaging modalities. Materials and methods: A systematic review following PRISMA guidelines was conducted across Web of Science, Scopus, and PubMed databases (January 2020–December 2025). CNN-based classification architectures were analyzed across ResNet, Vision Transformer, DenseNet, Xception, ConvNeXT, and Swin Transformer implementations, with comparative evaluation of accuracy and transfer learning performance. Object detection and segmentation approaches were examined across U-Net variants, R-CNN family algorithms, and YOLO-based implementations. Hyperparameter optimization strategies were assessed. Explainable AI methodologies including SHAP, Grad-CAM, DiCE, and LIME were evaluated for clinical interpretability and spatial localization accuracy. Results: Analysis of 320 publications revealed peak research activity in 2024 (63 publications, 19.7%). The United States led with 58 publications (18.1%), followed by China with 55 (17.2%). Multiparametric MRI constituted the primary imaging modality (42.5%), followed by histopathology (28.1%), ultrasound (14.1%), and PET imaging (9.4%). Vision Transformer models demonstrated the highest classification accuracy among evaluated architectures, while U-Net variants dominated segmentation applications with consistently high Dice coefficients. SHAP emerged as the most frequently adopted explainability method across the reviewed studies. Conclusions: CNN-based prostate cancer detection, classification, and segmentation demonstrate promise for improving diagnostic accuracy and clinical workflow efficiency, though challenges in dataset standardization, regulatory compliance, and clinical integration remain to be addressed.