Ciencias Exactas y Ciencias de la Salud

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An explainable AI-based system for kidney stone classification using color and texture descriptors
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) De Anda García, Ilse Karena; Ochoa Ruiz, Gilberto; emipsanchez; González Mendoza, Miguel; School of Engineering and Sciences; Campus Monterrey; Hinojosa Cervantes, Salvador Miguel
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.
Generation and evaluation of synthetic kidney stones images generated by diffusion models using limited data
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) González Pérez, Ruben; Ochoa Ruiz, Gilberto; emimmayorquin; Sánchez Ante, Gildardo; Daul, Christian; Hinojosa Cervantes, Salvador Miguel; School of Engineering and Sciences; Campus Monterrey; Falcón Morales, Luis Eduardo
One of the diseases that affects men and women around the world is kidney stones. Kidney stones are the accumulation of minerals inside the kidneys and can cause severe pain or problems with the urinary system. There are many different types of kidney stones and it is very important to identify and classify them to know which is the best treatment for it and to avoid relapses. Currently, there are very few specialists who can perform this analysis, since it is a complicated process that requires a lot of experience and the methods that are used currently to do this process can take a long time. Numerous studies have shown that deep learning methods hold great promise in automating the classification of kidney stones. These advanced algorithms leverage neural networks to analyze and interpret complex medical imaging data with high precision. By training on large datasets of annotated kidney stone images, deep learning models can learn to identify and classify different types of stones, such as calcium oxalate, uric acid, and struvite, with remarkable precision. Research has demonstrated that these models can achieve performance levels comparable to, and sometimes exceeding those of experienced radiologists. The ability of deep learning methods to process large amounts of data quickly and consistently makes them particularly valuable in clinical settings, where timely and accurate diagnosis is crucial. However, data scarcity represents a big challenge in using deep learning methods for kidney stone classification because Deep learning algorithms require large and diverse datasets to train effectively, capturing the wide variability in stone appearances and characteristics seen in different patients, but acquiring such extensive datasets in the medical field is difficult due to privacy concerns, the labor-intensive process of annotating medical images, and the relatively low prevalence of certain types of kidney stones. The objective of this study is to solve the problem of missing data through data augmentation using the SinDDM model, which is a diffusion model capable of generating synthetic images from a single training image. To evaluate the generated synthetic images, a case study was also carried out to compare the performance of a classifier when using generated images and when using only real images. The results obtained indicate an 11% improvement in the accuracy of the classifier, thus demonstrating that the proposed method is efficient in solving the data scarcity problem.

Ciencias Exactas y Ciencias de la Salud

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