ItemArticle

A Hybrid Deep-ensemble Decision-Support Framework for Reliable Early Breast Cancer Detection: A Cross-validated Outcome Analysis

Title
A Hybrid Deep-ensemble Decision-Support Framework for Reliable Early Breast Cancer Detection: A Cross-validated Outcome Analysis
Creator
Pachauri, Praveen; Upreti, Kamal; Kshirsagar, Pravin; Radhakrishnan, Ganeshavishwaa V.; Krishnan, Sivaneasan Bala; Kumar, Ajay; Jain, Rituraj
Description
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<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.
Source
Turk Onkoloji Dergisi;Volume;41;Issue;1;pp.43-51
Date
01-01-2026
Publisher
Istanbul Tip Fakultesi
Subject
Breast cancer detection; clinical decision support; diagnostic reliability; hybrid deepensemble learning
Coverage
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 & Telecommunication, J D College of Engineering & 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
Rights
All Open Access; Gold Open Access
Relation
ISSN: 13007467;
Format
online
Language
English
Type
Article

Citation

Pachauri, Praveen; Upreti, Kamal; Kshirsagar, Pravin; Radhakrishnan, Ganeshavishwaa V.; Krishnan, Sivaneasan Bala; Kumar, Ajay; Jain, Rituraj, “A Hybrid Deep-ensemble Decision-Support Framework for Reliable Early Breast Cancer Detection: A Cross-validated Outcome Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23695.

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