Gómez Vargas, GermánMichelucci, UmbertoLavarello Osorio, Giuseppe2026-03-112026-03-112025https://hdl.handle.net/11447/10622Degree project presented to the Faculty of Engineering of the Universidad del Desarrollo to qualify for the academic degree of Master in Data ScienceColorectal cancer (CRC) is characterized by significant tissue heterogeneity, making histopathological analysis a critical but complex task for diagnosis and prognosis. This project addresses the need for an automated classification system that is not only accurate but also interpretable, the most crucial factor for building trust and facilitating adoption in clinical settings. The proposed solution is twofold. First, it involves the fine-tuning of a domain-specific DenseNet-121 model, pre-trained with the vast KimiaNet histopathology image repository, on the public Kather et al. (2016) dataset of CRC textures. Second, to address the "black box" nature of Deep Learning models that limits their clinical utility, this study conducts a rigorous comparative analysis of advanced Explainable AI (XAI) techniques, including Grad-CAM, XGrad-CAM, and SmoothGrad. This analysis validates the model's reliability through both qualitative visual inspection and quantitative fidelity metrics. The central argument of this project is that a clinically useful classifier must be both accurate and trustworthy; this work presents a complete workflow to build and verify that trust.17 p.en070037SIntegrated gradientsHistopatologíaGrad-camxAIThesis