A Modular Framework for Brain Tumor Segmentation and Explainable-by-Design Classification from MRI

Brain tumor classification from Magnetic Resonance Imaging has achieved high predictive performance through deep data-driven models, which are often characterized by limited interpretability. In clinical settings, this lack of transparency is a critical limitation, as decision support systems must provide explicit and traceable reasoning processes. Most existing solutions address this issue through post-hoc explanation techniques applied to opaque models, without explicitly encoding decision logic within the model structure. This paper presents a modular framework for brain tumor detection and explainable-by-design classification from MRI images, explicitly separating tumor localization from decision making. Tumor regions are automatically detected and segmented using a YOLO-based instance segmentation model, while tumor classification is performed exclusively by a Prototype Tree-based classifier. Each prediction is produced through a single and explicit root-to-leaf decision path, ensuring intrinsic interpretability without relying on post-hoc explanation methods. The framework is evaluated end-to-end on a public brain tumor MRI dataset, considering multiple detector scales and Prototype Tree configurations. Experimental results show competitive classification performance on automatically extracted tumor regions, with a macro-averaged F1-score of approximately 0.87, while maintaining a compact and interpretable model structure with a maximum tree depth of five and thirty-seven nodes after pruning.