{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T06:26:47Z","timestamp":1789626407305,"version":"build-2803163510"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T00:00:00Z","timestamp":1766275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Bladder cancer represents a significant health problem worldwide, with it being a major cause of death and characterized by frequent recurrences. Effective treatment hinges on early and accurate diagnosis; however, traditional methods are invasive, time-consuming, and subjective. In this research, we propose a transparent deep learning model based on the YOLOv11 structure to not only enhance lesion detection but also give the visual support of the model\u2019s predictions. Five versions of YOLOv11\u2014nano, small, medium, large, and extra large\u2014were trained and tested by us on a comprehensive dataset of hematoxylin and eosin-stained histopathology slides with the inflammation, urothelial cell carcinoma (UCC), and invalid tissue categories. The YOLOv11-large variant turned out to be the best-performing model at the forefront of technology, with an accuracy of 97.09%, precision and recall of 95.47% each, and balanced accuracy of 96.60%. Besides the precision\u2013recall curves (AUPRC: inflammation = 0.935, invalid = 0.852, UCC = 0.958), ROC-AUC curves (overall AUC = 0.972) and risk\u2013coverage analysis (AUC = 0.994) were also used for detailed assessment of the model to confirm its steadiness and trustworthiness. The confusion matrix displayed the highest true positive rates in all classes and a few misclassifications, which mainly happened between inflammation and invalid samples, indicating a possible morphological overlap. Moreover, as supported by a low Expected Calibration Error (ECE), the model was in great calibration. YOLOv11 reaches higher performance while still being computationally efficient by incorporating advanced architectural features like the C3k2 block and C2PSA spatial attention module. This is a step towards the realization of the AI-assisted bladder cancer diagnostic system that is not only reliable and transparent but also scalable, presented in this work.<\/jats:p>","DOI":"10.3390\/bioengineering13010004","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T11:30:18Z","timestamp":1766489418000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis"],"prefix":"10.3390","volume":"13","author":[{"given":"Abdallah A.","family":"Mohamed","sequence":"first","affiliation":[{"name":"Department of Information Systems, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia"},{"name":"Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Menoufia 32511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1721-481X","authenticated-orcid":false,"given":"Yousry","family":"AbdulAzeem","sequence":"additional","affiliation":[{"name":"School of Computational Sciences and Artificial Intelligence, Zewail City of Science, Technology and Innovation, Giza 12578, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullateef I.","family":"Almudaifer","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanaa","family":"ZainEldin","sequence":"additional","affiliation":[{"name":"Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0686-4411","authenticated-orcid":false,"given":"Hossam Magdy","family":"Balaha","sequence":"additional","affiliation":[{"name":"Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt"},{"name":"Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0120-3235","authenticated-orcid":false,"given":"Mahmoud","family":"Badawy","sequence":"additional","affiliation":[{"name":"Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt"},{"name":"Department of Computer Science and Information, Applied College, Taibah University, Medinah 41461, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1259-6193","authenticated-orcid":false,"given":"Mostafa A.","family":"Elhosseini","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia"},{"name":"Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"He, K., Meng, X., Wang, Y., Feng, C., Liu, Z., Li, Z., and Niu, Y. 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