XEns-CKD: An Explainable Ensemble-Based Approach for Chronic Kidney Disease Stage Detection

2026-08-02

AI Summary

This study addresses the challenge of early chronic kidney disease (CKD) detection, which is hindered by its asymptomatic onset and consequent delays in staging and intervention. To overcome this, the authors propose a novel approach that integrates an ensemble of Vision Transformers (ViTs) with multiple interpretable AI techniques to enable precise classification of ultrasound images across normal kidneys and all five CKD stages—a first in the field. The method leverages LIME, Layer-wise Relevance Propagation (LRP), and a newly introduced Attention-Min/Max fusion strategy to effectively localize and interpret diagnostically critical regions. Evaluated on a private renal ultrasound dataset, the model achieves an overall accuracy of 86.36%, outperforming existing methods by 4% and demonstrating superior performance across multiple macro-level metrics while maintaining both high diagnostic accuracy and strong interpretability.

Abstract

Chronic kidney disease (CKD) is a silent disease. Its progression may not significantly hamper a person's daily routine. Human kidney function can be classified as normal or as one of the five stages of CKD. Early detection of the CKD stage can help patients understand the functional status of their kidneys and follow medical advice to slow CKD progression. In this paper, we propose XEns-CKD, a novel ensemble vision transformer-based scheme for CKD stage classification using ultrasound images. Three ViTs were trained on a private ultrasound image dataset using different training parameters. The performance of each ViT was evaluated using macro sensitivity, macro specificity, macro precision, macro F1-score, macro Youden index, the Matthews correlation coefficient (MCC), and macro balanced accuracy. The ensemble model achieved an overall classification accuracy of 86.36%. This work also emphasizes identifying and interpreting kidney regions affected by CKD progression. Explainable artificial intelligence techniques, including LIME, LRP, Attention-Min, and Attention-Max, were used to improve model transparency and clinical trust. An attention map combining the Attention-Min and Attention-Max results effectively identified and interpreted kidney regions affected during CKD progression from one stage to another. The attention map also highlighted the effects of CKD progression in these regions. Compared with existing methods, the proposed method classified the five CKD stages and normal kidney status with a 4% improvement in accuracy.

Problem

• Chronic Kidney Disease
• CKD staging
• Ultrasound image classification
• Early detection
• Kidney function assessment

Innovation

• Ensemble Vision Transformer
• Explainable AI
• Chronic Kidney Disease staging
• Ultrasound image analysis
• Attention map interpretation