Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Pachauri, Praveen; Upreti, Kamal; Kshirsagar, Pravin; Radhakrishnan, Ganeshavishwaa V.; Krishnan, Sivaneasan Bala; Kumar, Ajay; Jain, Rituraj Title A name given to the resource A Hybrid Deep-ensemble Decision-Support Framework for Reliable Early Breast Cancer Detection: A Cross-validated Outcome Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Turk Onkoloji Dergisi;Volume;41;Issue;1;pp.43-51 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.5505/tjo.2026.4798" target="_blank" rel="noreferrer noopener">https://doi.org/10.5505/tjo.2026.4798</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032789771?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032789771?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Pachauri P., Department of Computer Science, Government Polytechnic Siwan, Siwan, India; Upreti K., Department of Computer Science, Christ University, Ghaziabad, India; Kshirsagar P., Department of Electronics &amp; Telecommunication, J D College of Engineering &amp; Management, Nagpur, India; Radhakrishnan G.V., Department of Economics and Finance, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Krishnan S.B., Singapore Institute of Technology Engineering Cluster, Singapore, Singapore; Kumar A., Dev Bhoomi Uttarakhand University, Dehradun, India; Jain R., Department of Information Technology, Marwadi University, Rajkot, India Description An account of the resource OBJECTIVE The necessity to diagnose breast cancer early and correctly is the need to minimize the diagnostic uncertainty and unwarranted clinical procedures. This paper assesses the reliability of a hybrid deep-ensemble decision-support model in terms of diagnostic reliability, stability of outcome, and translational feasibility of the model via structured clinical data to detect early breast cancer. METHODS The Wisconsin Diagnostic Breast Cancer dataset which consisted of 569 cases of benign and malignant tumors was analyzed retrospectively. The framework proposed combines the deep learning of latent representations with stacked classification, ensemble-based feature selection, and stacked classification. Performance evaluation was performed based on sensitivity, specificity, accuracy, F1-score, and area under the curve (AUC) performed using stratified 10-fold cross-validation. The statistical stability across folds and the comparison with baseline models were determined with the help of non-parametric tests (p&lt;0.05). RESULTS The model had good diagnostic performance with an accuracy of between 91.2-100 (Mean 96), Sensitivity of 76.2-100, good specificity value, and AUC 0.973-1.000. Variability in performance between folds was low, and statistically significant enhancement as compared to baseline classifiers were present. CONCLUSION The hybrid deep-ensemble model is highly diagnostic, has robust discriminative ability, and ultimately remains stable, which demonstrates the methodological robustness and diagnostic reliability of the proposed framework as a proof-of-concept decision-support model for early breast cancer detection, with potential translational relevance subject to further external clinical validation. 2026, Turkish Society for Radiation Oncology. Subject The topic of the resource Breast cancer detection; clinical decision support; diagnostic reliability; hybrid deepensemble learning Publisher An entity responsible for making the resource available Istanbul Tip Fakultesi Relation A related resource ISSN: 13007467; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online