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.
Image captioning for automated grading and understanding of pre-cancerous inflammations in ulcerative colitis on endoscopic images
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Valencia Velarde, Flor Helena; Ochoa Ruiz, Gilberto; emimmayorquin; Hinojosa Cervantes, Salvador Miguel; Gonzalez Mendoza, Miguel; School of Engineering and Sciences; Campus Monterrey; Ali, Sharib
This thesis presents the development and results of an automated system for grading and understanding ulcerative colitis (UC) through image captioning. UC is a chronic inflammatory disease of the large intestine, characterized by alternating periods of remission and relapse. The conventional method for assessing UC severity involves the Mayo Endoscopic Scoring (MES) system, which depends on the visual evaluation of mucosal characteristics. This method is subjective and can result in considerable variability between different observers. The primary objective of this thesis is to investigate and evaluate contemporary methodologies for developing an image captioning model that can generate MES scores and descriptive captions for mucosal features observed in endoscopic images. This research involved an extensive examination of various convolutional neural networks (CNNs) for visual feature extraction and the implementation of several sequence models for natural language processing (NLP), including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNNs). Our system was rigorously evaluated on a dataset consisting of 982 images obtained from both public repositories and proprietary collections. The combination of DenseNet121 for CNN-based feature extraction and 2 layers GRU for sequence generation yielded the best performance, achieving a BLEU-4 score of 0.7352. This high level of similarity between the reference and predicted captions indicates the model’s effectiveness in accurately capturing and describing critical mucosal features necessary for UC grading. While our system performed well in predicting MES-0 to MES-2 categories, it encountered challenges in accurately predicting MES-3 classifications. This discrepancy is likely due to the underrepresentation of severe cases in the training dataset. Despite this limitation, the system’s ability to generate comprehensive descriptions of mucosal features represents a significant advancement in the automated evaluation of UC. The contributions of this thesis include the creation of a dataset for UC captioning task, a detailed analysis of various CNN architectures and sequence models, an extensive evaluation of their performance, and the development of a robust framework for automated UC grading and description generation. Our findings suggest that combining advanced visual feature extraction techniques with sophisticated NLP models can significantly improve the accuracy and reliability of automated medical diagnosis systems. By reducing inter-observer variability and providing a valuable tool for training new clinicians, this automated grading and captioning system has the potential to enhance diagnostic accuracy and clinical decision-making in UC management. This work represents a substantial step forward in the field of endoscopic imaging, underscoring the importance of integrating machine learning techniques in clinical practice. Additionally, by generating detailed descriptions, this approach helps mitigate the “black box” nature of deep learning, offering more transparency and interpretability in automated medical diagnoses.

Ciencias Exactas y Ciencias de la Salud

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