{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:21:50Z","timestamp":1785410510538,"version":"3.56.0"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005911","name":"Najran University","doi-asserted-by":"publisher","award":["NU\/CPL\/MRC\/14\/3155-1"],"award-info":[{"award-number":["NU\/CPL\/MRC\/14\/3155-1"]}],"id":[{"id":"10.13039\/501100005911","id-type":"DOI","asserted-by":"publisher"}]},{"award":["NU\/CPL\/MRC\/14\/3155-1"],"award-info":[{"award-number":["NU\/CPL\/MRC\/14\/3155-1"]}],"id":[{"id":"https:\/\/ror.org\/05edw4a90","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: The steady rise of prostate cancer in Saudi Arabia signals a critical public health shift that requires immediate investment in early detection and prevention to mitigate a future clinical crisis. Accurate diagnosis using multiparametric MRI and PI-RADS scoring remains challenging, as interpretations are highly experience-dependent and subspecialized radiologists are limited. Methods: To address this gap, this study introduces a novel Dual-Stream Attention Network designed to automate the classification of low-risk (PIRADS 2-3) versus high-risk (PIRADS 4-5) lesions from T2-weighted MRI. Leveraging a ResNet50 backbone, the architecture employs parallel streams for Local and Global Feature Processing, each enhanced by a Channel-Spatial Attention module to highlight diagnostically relevant regions. These features are integrated through a Cross-Stream Fusion mechanism and a gate-controlled Adaptive Feature Fusion module to optimize multi-scale information. The model was developed and validated on a regional dataset of 3850 images from Jazan Specialist Hospital and Prince Mohammed bin Naser Hospital. This research provides a standardized, high-precision diagnostic path tailored to the Saudi Arabian population, conducted under institutional review board approval (No. 25138). Results: The proposed dual-stream attention network achieved an accuracy of 97.8% on the validation set and 96.4% on the test set, demonstrating high performance and generalization capabilities in classifying prostate lesions from Saudi patient populations. Conclusions: The proposed dual-stream architecture with novel attention and fusion mechanisms demonstrates high effectiveness for prostate cancer classification from T2-weighted MRI in Saudi clinical settings. This represents the first deep learning model specifically trained and validated on Saudi Arabian prostate MRI data, with the potential to address the shortage of specialized expertise and improve diagnostic efficiency in the Kingdom.<\/jats:p>","DOI":"10.3390\/jcm15093327","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:55:39Z","timestamp":1777370139000},"page":"3327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Prostate Cancer Detection on T2-Weighted MRI Using a Dual-Stream Attention Network: A Study on Private Saudi Clinical Data and Public Benchmark Datasets"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3354-5018","authenticated-orcid":false,"given":"Saeed","family":"Alqahtani","sequence":"first","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia"},{"name":"Health Research Center, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3210-6503","authenticated-orcid":false,"given":"M. A.","family":"Jowhari","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia"},{"name":"Health Research Center, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yahya.Q.","family":"Sabi","sequence":"additional","affiliation":[{"name":"Radiology Department, Prince Mohammed bin Nasser Hospital (PMNH), Ministry of Health (MOH), Jazan 82911, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6486-6450","authenticated-orcid":false,"given":"Hussein","family":"Alshaari","sequence":"additional","affiliation":[{"name":"Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia"},{"name":"Health Research Center, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Singh, A., Randive, S., Breggia, A., Ahmad, B., Christman, R., and Amal, S. (2023). Enhancing prostate cancer diagnosis with a novel artificial intelligence-based web application: Synergizing deep learning models, multimodal data, and insights from usability study with pathologists. Cancers, 15.","DOI":"10.3390\/cancers15235659"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"He, M., Cao, Y., Chi, C., Yang, X., Ramin, R., Wang, S., Yang, G., Mukhtorov, O., Zhang, L., and Kazantsev, A. (2023). Research progress on deep learning in magnetic resonance imaging\u2013based diagnosis and treatment of prostate cancer: A review on the current status and perspectives. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1189370"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7877","DOI":"10.1007\/s00521-024-09499-z","article-title":"Prostate cancer grading framework based on deep transfer learning and Aquila optimizer","volume":"36","author":"Balaha","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14173","DOI":"10.1007\/s11042-023-15793-0","article-title":"A novel deep learning-based technique for detecting prostate cancer in MRI images","volume":"83","author":"Singh","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sekhoacha, M., Riet, K., Motloung, P., Gumenku, L., Adegoke, A., and Mashele, S. (2022). Prostate cancer review: Genetics, diagnosis, treatment options, and alternative approaches. Molecules, 27.","DOI":"10.3390\/molecules27175730"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhu, M., Sali, R., Baba, F., Khasawneh, H., Ryndin, M., Leveillee, R.J., Hurwitz, M.D., Lui, K., Dixon, C., and Zhang, D.Y. (2024). Artificial intelligence in pathologic diagnosis, prognosis and prediction of prostate cancer. Am. J. Clin. Exp. Urol., 12.","DOI":"10.62347\/JSAE9732"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.euo.2023.12.005","article-title":"Comparing the performance of digital rectal examination and prostate-specific antigen as a screening test for prostate cancer: A systematic review and meta-analysis","volume":"7","author":"Matsukawa","year":"2024","journal-title":"Eur. Urol. Oncol."},{"key":"ref_8","first-page":"921","article-title":"MRI as a screening tool for prostate cancer: Current evidence and future challenges","volume":"41","author":"Chandrasekar","year":"2023","journal-title":"World J. Urol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1038\/s41391-024-00884-2","article-title":"Biopsy strategies in the era of mpMRI: A comprehensive review","volume":"28","author":"Windisch","year":"2025","journal-title":"Prostate Cancer Prostatic Dis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6729","DOI":"10.1016\/j.acra.2025.05.062","article-title":"Radiomics for Detection of Prostate Cancer in PI-RADS 3 Lesions: A Systematic Review and Meta-analysis","volume":"32","author":"Zhang","year":"2025","journal-title":"Acad. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1038\/s41391-024-00904-1","article-title":"Follow-up on patients with initial negative mpMRI target and systematic biopsy for PI-RADS \u2265 3 lesions\u2013an EAU-YAU study enhancing prostate cancer detection","volume":"28","author":"Zattoni","year":"2025","journal-title":"Prostate Cancer Prostatic Dis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"27085","DOI":"10.1109\/ACCESS.2021.3057654","article-title":"Prostate cancer detection using deep learning and traditional techniques","volume":"9","author":"Iqbal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Olawuyi, O., and Viriri, S. (2025). Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art. J. Imaging, 11.","DOI":"10.3390\/jimaging11080254"},{"key":"ref_14","unstructured":"Erdem, E., and Bozkurt, F. (2021). A comparison of various supervised machine learning techniques for prostate cancer prediction. Avrupa Bilim Teknol. Derg., 610\u2013620."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hun, C.C., Yazid, H., Safar, M.J.A., and Ab Rahman, K.S. (2022). Comparison between k-nearest neighbor (KNN) and decision tree (DT) classifier for glandular components. Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications: Enhancing Research and Innovation Through the Fourth Industrial Revolution, Springer.","DOI":"10.1007\/978-981-16-8129-5_46"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsm.2022.10.001","article-title":"Application of support vector machine algorithm for early differential diagnosis of prostate cancer","volume":"6","author":"Akinnuwesi","year":"2023","journal-title":"Data Sci. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"161.e1","DOI":"10.1016\/j.urolonc.2021.08.007","article-title":"A machine learning approach to predict progression on active surveillance for prostate cancer","volume":"Volume 40","author":"Nayan","year":"2022","journal-title":"Proceedings of the Urologic Oncology: Seminars and Original Investigations"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.future.2021.09.030","article-title":"Prostate cancer classification from ultrasound and MRI images using deep learning based Explainable Artificial Intelligence","volume":"127","author":"Hassan","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_19","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Barzekar, H., and Yu, Z. (2022). C-Net: A reliable convolutional neural network for biomedical image classification. Expert Syst. Appl., 187.","DOI":"10.1016\/j.eswa.2021.116003"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, W., Li, C., Xu, N., Jiang, T., Rahaman, M.M., Sun, H., Wu, X., Hu, W., Chen, H., and Sun, C. (2022). CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron. Pattern Recognit., 130.","DOI":"10.1016\/j.patcog.2022.108829"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, Y., Xin, Y., Zhang, Y., Yang, D., and Xu, T. (2023). MESTrans: Multi-scale embedding spatial transformer for medical image segmentation. Comput. Methods Programs Biomed., 233.","DOI":"10.1016\/j.cmpb.2023.107493"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Salman, M.E., \u00c7akar, G.\u00c7., Azimjonov, J., K\u00f6sem, M., and Cedimo\u011flu, \u0130.H. (2022). Automated prostate cancer grading and diagnosis system using deep learning-based Yolo object detection algorithm. Expert Syst. Appl., 201.","DOI":"10.1016\/j.eswa.2022.117148"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, X., Li, L., Li, M., Yan, P., Feng, T., Luo, H., Zhao, Y., and Yin, S. (2025). Knowledge distillation and teacher-student learning in medical imaging: Comprehensive overview, pivotal role, and future directions. Med. Image Anal., 107.","DOI":"10.1016\/j.media.2025.103819"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Naik, N., Tokas, T., Shetty, D.K., Hameed, B.Z., Shastri, S., Shah, M.J., Ibrahim, S., Rai, B.P., Ch\u0142osta, P., and Somani, B.K. (2022). Role of deep learning in prostate cancer management: Past, present and future based on a comprehensive literature review. J. Clin. Med., 11.","DOI":"10.3390\/jcm11133575"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, F., Zhao, Y., Song, J., Tu, G., Liu, Y., Peng, Y., Mao, J., Yan, C., and Wang, R. (2024). A hybrid classification model with radiomics and CNN for high and low grading of prostate cancer Gleason score on mp-MRI. Displays, 83.","DOI":"10.1016\/j.displa.2024.102703"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1002\/jmri.27204","article-title":"Deep-learning-based artificial intelligence for PI-RADS classification to assist multiparametric prostate MRI interpretation: A development study","volume":"52","author":"Sanford","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1148\/radiol.2019190938","article-title":"Classification of cancer at prostate MRI: Deep learning versus clinical PI-RADS assessment","volume":"293","author":"Schelb","year":"2019","journal-title":"Radiology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106918","DOI":"10.1016\/j.rineng.2025.106918","article-title":"An Enhanced Deep Learning Framework for Prostate Cancer Detection Using Modified VGG16 and LeNet-MobileNetV2 Integration","volume":"27","author":"Nandhini","year":"2025","journal-title":"Results Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1007\/s11548-018-1841-4","article-title":"Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging","volume":"13","author":"To","year":"2018","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, Z., Zhang, M., and Gao, S. (2022). Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.994950"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8712","DOI":"10.1109\/JIOT.2025.3641888","article-title":"PHH-FL: Perceptual Hashing Hypernetwork Personalized Federated Learning for Heterogeneous Medical Image Analysis Tasks","volume":"13","author":"Sun","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Maheriya, K., Rahevar, M., Parmar, M., Kothadiya, D., Patel, A., and Ganatra, A. (2025). Revolutionizing Prostate Cancer Diagnosis: Vision Transformers with Explainable Artificial Intelligence to Accurate and Interpretable Prostate Cancer Identification. Explainable Artificial Intelligence in Medical Imaging, Auerbach Publications.","DOI":"10.1201\/9781032626345-12"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Leo, E.S.A., Kannan, K.N., Thangarasu, G., and Subramanian, K. (2025). A Deep Auto-Encoder Approach for Prostate Cancer Detection Using Meta-Hybrid Learning and Ensemble Methods. Proceedings of the 2025 IEEE 15th Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE.","DOI":"10.1109\/ISCAIE64985.2025.11080807"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dimitriadis, A., Kalliatakis, G., Osuala, R., Kessler, D., Mazzetti, S., Regge, D., Diaz, O., Lekadir, K., Fotiadis, D., and Tsiknakis, M. (2025). Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks. J. Imaging, 11.","DOI":"10.3390\/jimaging11040098"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1038\/s41391-025-00957-w","article-title":"A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological images","volume":"28","author":"Chaurasia","year":"2025","journal-title":"Prostate Cancer Prostatic Dis."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Debnath, J., Mohiuddin, A.B., Sakib, A., Hossain, A., Shanto, M.M., Forhad, S., Ahmed, M.R., Haque, R., and Rahman, S. (2025). Hybrid Vision Transformer Model for Accurate Prostate Cancer Classification in MRI Images. Proceedings of the 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), IEEE.","DOI":"10.1109\/ECCE64574.2025.11013952"},{"key":"ref_38","first-page":"3799","article-title":"Artificial intelligence based prostate cancer classification model using biomedical images","volume":"72","author":"Alshahrani","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, G., Gao, L., Liu, H., Wang, S., Xu, X., and Zhao, B. (2025). Texture graph transformer for prostate cancer classification. Biomed. Signal Process. Control, 99.","DOI":"10.1016\/j.bspc.2024.106890"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jihad, S.M., Aalsaud, A., Almukhtar, F.H., Kareem, S., and Yousif, R.Z. (2025). Investigating feature extraction by SIFT methods for prostate cancer early detection. Egypt. Inform. J., 29.","DOI":"10.1016\/j.eij.2024.100607"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mittal, P., Sharma, B., Yadav, D., Sharma, T., and Dhaou, I.B. (2025). Comparative Analysis of Decision Tree and Random Forest Models for Prostate Cancer Classification. Proceedings of the International Conference on Artificial Intelligence and Knowledge Processing, Springer.","DOI":"10.1007\/978-3-032-14706-6_3"},{"key":"ref_42","unstructured":"Tavara, A.F.A., Kumar, U., Pradeepkumar, J., and Sun, J. (2025). Prostate-VarBench: A benchmark with interpretable TabNet framework for prostate cancer variant classification. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Ahn, E., Feng, D., Khadra, M., and Kim, J. (2025). Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI. Comput. Med. Imaging Graph., 122.","DOI":"10.1016\/j.compmedimag.2025.102510"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chrysostomou, C., and Dovrolis, C. (2025). Multi-modal Deep Learning with Spatial Transformers for Biparametric MRI Prostate Cancer Classification. Proceedings of the International Conference on AI in Healthcare, Springer.","DOI":"10.1007\/978-3-032-00652-3_29"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"115735","DOI":"10.1109\/ACCESS.2025.3585236","article-title":"Multi-modal Analysis of Bi-Parametric MRI Slices for Lesion Detection in Prostate Cancer Screening","volume":"13","author":"Antonutti","year":"2025","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Abdallah, N., Marion, J.M., Taguelmimt, K., and Bert, J. (2025). Unveiling the role of harmonization on clinically significant prostate cancer detection using MRI. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-22433-0"},{"key":"ref_47","unstructured":"Ortiz, M., Olmos, J., and Mart\u00ednez, F. (2025). A Second-Order Attention Mechanism For Prostate Cancer Segmentation and Detection in Bi-Parametric MRI. arXiv."},{"key":"ref_48","unstructured":"Trombetta, R., Rouvi\u00e8re, O., and Lartizien, C. (2024). Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1007\/s10278-025-01581-9","article-title":"Few-shot learning for prostate cancer detection on MRI: Comparative analysis with radiologists\u2019 performance","volume":"39","author":"Yamagishi","year":"2025","journal-title":"J. Imaging Inform. Med."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/9\/3327\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T10:30:52Z","timestamp":1777372252000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/9\/3327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,27]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jcm15093327"],"URL":"https:\/\/doi.org\/10.3390\/jcm15093327","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,27]]}}}