Explainability‐Aligned Reliability‐Weighted Fuzzy Ensemble for Automated Cervical Cancer Classification

Abstract Cervical cancer remains a major global health concern, highlighting the need for computer‐aided diagnostic systems that are both reliable and interpretable. Despite advances in deep learning–based cytology image classification, a gap persists in aligning model predictions with biologically meaningful explanations. This study aims to develop an explainability‐aligned, sample‐wise reliability‐weighted fuzzy ensemble framework for cervical cytology image classification to enhance both performance and interpretability. Methods The proposed framework integrates three pretrained convolutional neural network backbones—InceptionV3, MobileNetV2, and Inception‐ResNetV2—within a fuzzy ensemble structure. A novel explainability metric, termed Explainable Artificial Intelligence Alignment (XAIHit), is introduced to quantitatively assess the spatial correspondence between Grad‐CAM activation maps and annotated cytoplasmic and nuclear regions. The model combines calibrated confidence estimates with XAIHit to produce a per‐sample reliability score that guides fuzzy aggregation, ensuring anatomically informed and statistically robust decision‐making. Experiments were conducted on the SIPaKMeD dataset. Results The proposed ensemble achieved strong predictive performance, with accuracy ≈ 0.94, F1‐score ≈ 0.94, and area under the curve (AUC) ≈ 0.99. Calibration metrics further confirmed model reliability, with an expected calibration error (ECE) of 0.030, a Brier score of 0.078, and a negative log‐likelihood (NLL) of 0.198. The approach consistently outperformed conventional deep learning and fuzzy ensemble baselines. Conclusions This study presents an interpretable and reliability‐aware fuzzy ensemble framework that advances AI‐assisted cervical cancer screening. By integrating explainability alignment and calibrated confidence into a unified reliability measure, the method fosters both diagnostic accuracy and clinical trust, marking a significant step toward safe, transparent medical AI systems. Comparable performance was also observed on an independent external validation dataset, confirming the cross‐dataset generalization capability of the proposed framework.