An explainable AI-based system for kidney stone classification using color and texture descriptors

Abstract

Kidney stone disease affects nearly 10% of the global population and remains a significant clinical and economic burden. Accurate classification of stone subtypes is essential for guiding treatment decisions and preventing recurrence. This thesis presents the design, implementation, and evaluation of an explainable artificial intelligence (XAI)-based dual-output system that predicts both the texture and color subtype of kidney stones using image-based descriptors. The proposed system extracts features from stone images captured in Section and Surface views and processes them through parallel branches optimized for texture and color. Texture classification is performed using an ensemble of PCA-reduced deep descriptors from InceptionV3, AlexNet, and VGG16. For color, the most effective model combined handcrafted HSV descriptors with PCA-compressed deep CNN features. These were fused into a dual-output architecture using a MultiOutputClassifier framework. The models were evaluated using five-fold cross-validation. Texture classification reached 98.67% ± 1.82 accuracy in Section and 95.33% ± 1.83 in Surface. Color classification achieved 90.67% ± 9.25 and 85.34% ± 11.93, respectively. Exact match accuracy for joint prediction was 91.4% in Section and 84.2% in Surface, indicating high coherence between the two outputs. Explainability was addressed through FullGrad visualizations and Weight ofFeature (WOF) analysis, both of which showed that the model relied on clinically meaningful image regions and that color features held slightly greater predictive influence. Compared to state-of-the-art approaches, including multi-view fusion models, the proposed method achieved a competitive performance while maintaining a modular and transparent structure. The findings validate the hypothesis that combining deep and handcrafted descriptors can enhance interpretability and, in some cases, performance. This work contributes a clinically aligned and interpretable framework for automated kidney stone classification and supports the integration of XAI into nephrological diagnostic workflows. Moreover, by providing interpretable dual predictions of color and texture, this system can support early preventive decisions aimed at reducing recurrence. Future work could explore advanced generative models to further expand diversity and clinical utility of synthetic data. Compared to state-of-the-art approaches, the proposed method achieved a competitive performance while maintaining a modular and transparent structure. The findings validate the hypothesis that combining deep and handcrafted descriptors can enhance interpretability and performance. This work contributes a clinically aligned and interpretable framework for automated kidney stone classification and supports the integration of XAI into nephrological diagnostic workflows.