{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T08:03:35Z","timestamp":1780473815956,"version":"3.54.1"},"reference-count":37,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp; Technique"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Prostate cancer is a prevalent and serious health concern, ranking among the most frequently diagnosed cancers and a leading cause of cancer\u2010related deaths in men worldwide. Early detection and accurate diagnosis are crucial for improving patient outcomes by limiting disease progression. Histopathological image analysis remains the gold standard for prostate cancer detection; however, manual interpretation is time\u2010consuming and requires specialized expertise. To address these challenges, this study proposes a hybrid deep learning framework that combines an ensemble of transfer\u2010learned CNNs (VGG\u201016, DenseNet\u2010121, and AlexNet) with a fine\u2010tuned Vision Transformer (ViT). The CNN ensemble extracts rich local features, while the ViT captures global contextual dependencies through a self\u2010attention mechanism and a multilayer perceptron (MLP). Additionally, a cross\u2010attention fusion (CAF) module integrates local and global features, and knowledge distillation (KD) enables a lightweight student network suitable for efficient clinical deployment. The study utilizes the publicly available PANDA dataset for training and testing. Preprocessing steps, including patch generation, gamma correction, and stain deconvolution, enhance image quality and feature representation. A comprehensive evaluation was conducted using standard performance metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), precision, F1\u2010score, false negative rate (FNR), and false positive rate (FPR). An ablation study confirmed the contribution of each module, highlighting the critical role of ensemble CNNs, CAF, and ViT in improving performance. Experimental results demonstrate that the proposed model outperforms conventional transfer learning models and existing state\u2010of\u2010the\u2010art techniques, achieving 97.91% accuracy, along with significant improvements in TPR, TNR, and reduced FNR\/FPR. The computational complexity, evaluated in terms of parameters, FLOPs, GPU memory, and inference time, indicates that the proposed model is more demanding than traditional CNNs. Nevertheless, the architecture strikes a practical balance between predictive accuracy and efficiency, making it suitable for real\u2010world clinical applications. These findings underscore the potential of AI\u2010powered hybrid models in expediting prostate cancer diagnosis and enabling timely intervention for improved patient outcomes.<\/jats:p>","DOI":"10.1002\/jemt.70104","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:55:41Z","timestamp":1765439741000},"page":"687-704","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accelerating Prostate Cancer Detection Through Histopathological Image Analysis Using Artificial Intelligence"],"prefix":"10.1002","volume":"89","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3020-0594","authenticated-orcid":false,"given":"Anandh Sam Chandra","family":"Bose","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, College of Applied Sciences AlMaarefa University  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0841-8351","authenticated-orcid":false,"given":"Chandran","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Department of EEE Rajalakshmi Engineering College  Chennai Tamilnadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandrasekaran","family":"Saravanakumar","sequence":"additional","affiliation":[{"name":"Department of ECE SRM Valliammai Engineering College  Chennai Tamilnadu India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"e_1_2_9_2_1","article-title":"Tumor\u2010Centered Patching for Enhanced Medical Image Segmentation","author":"Asghar M.","year":"2023","journal-title":"arXiv Preprint 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