XAIHO: explainable AI leveraging hybrid optimized framework for liver cirrhosis detection

Abstract This study introduces an explainable AI leveraging hybrid optimized framework for liver cirrhosis detection (XAIHO) with deep learning (DL) to address the critical challenges of low interpretability and diagnostic inefficiency in traditional and AI-driven liver cirrhosis detection systems. Conventional approaches often rely on invasive procedures and lack transparency, while existing DL models, although accurate, function as black boxes and limit clinical trust. To bridge this gap, the research initially explored machine learning (ML) models and then integrated XAI techniques to improve model explainability. Subsequently, DL approaches were employed using fine-tuned pre-trained models such as VGG16, VGG19, ResNet50, ResNet101, Xception, Inception-V3, EfficientNetB1, EfficientNetB2, Vision Transformer (ViT), and InceptionResNetV2. While these models showed strong classification performance, their limited interpretability remained a barrier for clinical deployment. To address this, the proposed XAIHO framework was developed in two phases: first implementing XAI without optimizers, and then enhancing it with advanced optimizers (Adam, NAdam, RMSProp) to improve both predictive accuracy and interpretability. The proposed XAIHO framework achieves a peak accuracy of 92.35%, representing a 4% improvement over standard DL models and an 8% increase compared to traditional ML baselines. Additionally, transparency and interpretability are significantly improves using SHAP values and attention-based visualizations, providing meaningful insights into critical features such as bilirubin, albumin, and age. Empirical results, validate through multiple performance metrics, confirm the framework’s potential for accurate, transparent, and clinically applicable liver cirrhosis diagnosis.