An Explainable Histopathological Nuclei Classification System Based on Fuzzy Decision Trees

The automatic classification of cell nuclei in histopathological images constitutes a fundamental component in the development of computer-aided diagnosis systems, offering valuable support in clinical decision-making and treatment planning. Despite the notable performance achieved by Deep Learning (DL) models in this domain, their limited interpretability remains a significant barrier to their adoption, especially in safety-critical fields such as healthcare. This study presents an explainable Nucleus Classification (NuC) system based on Fuzzy Decision Tree (FDT). The proposed approach leverages as input a set of human-interpretable numerical features extracted from the images of segmented nuclei. The system is evaluated on the PanNuke dataset, with a specific focus on the testicular tissue subset, and benchmarked against a Multi-Layer Perceptron (MLP) employed as a reference opaque model. Experimental results indicate that the FDT-based system attains competitive classification performance while offering intrinsically interpretable, rule-based outputs. In addition, we perform an explainability analysis demonstrating the proposed model’s capacity to generate linguistically meaningful rules that are consistent with domain-specific histopathological knowledge.