A Clinician-Configurable FAHP-Based Ensemble Framework for Multi-Label Chest X-Ray Classification

Background/Objectives: Deep learning-based chest X-ray (CXR) analysis has shown strong diagnostic performance; however, conventional ensemble methods typically rely on equal-weight aggregation and do not account for disease-specific model behavior or clinician-defined priorities.

Methods

We developed a clinician-configurable ensemble framework based on the fuzzy analytic hierarchy process (FAHP) for multi-label CXR classification. The proposed method employs a two-stage design consisting of disease-specific model weighting using multi-criteria decision analysis and utility-guided seed aggregation based on ΔNet Benefit (ΔNB). Five base architectures, including Vision Transformer (ViT), SwinV2 Transformer, DenseNet121, DenseNet121 + ViT and DenseNet121 + SwinV2, were trained on the National Institutes of Health (NIH) ChestX-ray14 dataset using 10 random seeds. Performance was evaluated on a held-out test set using area under the receiver operating characteristic curve (AUROC), area under the precision–recall curve (AUPRC), balanced accuracy, and F1 score and compared with single-model baselines, Hard Voting, and Soft Voting.