Kabir, Md SaifNoor, AbtahiChaman, Ummay MaimonaMehreen, Nafisa2026-08-102026-08-102026-06-11M. S. Kabir, A. Noor, U. M. Chaman and N. Mehreen, "Robust Multi-Backbone Hybrid Fusion for Chest X-Ray Pneumonia Detection," 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN), Chittagong, Bangladesh, 2026, pp. 1-6, doi: 10.1109/QPAIN69676.2026.11545869.979-833154990-9https://hdl.handle.net/10361/28867Chest X-ray imaging is extensively used for the diagnosis of pneumonia; however, manual interpretation is timeconsuming and prone to inter-observer variability. To overcome the limitations of fully supervised approaches and to improve stability, this paper introduces a multi-backbone hybrid framework that integrates supervised classification with unsupervised anomaly detection for pneumonia identification. The proposed method combines a supervised CNN-based classifier with a PatchCore anomaly detection model utilizing multiple pretrained backbones, including ResNet, DenseNet, and EfficientNet. Classifier confidence, predictive uncertainty, and PatchCore anomaly scores are fused using a logistic regression model trained exclusively on validation data, while strict leakage-free evaluation is ensured by keeping the test set fully locked. Experimental results on a publicly available chest X-ray dataset demonstrate that the proposed framework consistently achieves fused ROC-AUC scores exceeding 0.97 with low variance across five independent random seeds, indicating strong robustness, reproducibility, and suitability for reliable pneumonia screening.6 pagesen-USAnomaly detectionDenseNetEfficient-NetPatchcoreResNetComputer networks--Security measures.Machine learning.Artificial Intelligence.Molecular diagnosis.Conference Proceedings10.1109/QPAIN69676.2026.11545869