MorphXAI: An Explainable Framework for Morphological Analysis of Parasites in Blood Smear Images
This work addresses the need for clinically useful, interpretable AI in diagnosing parasitic infections, particularly in low-resource settings, by providing morphological insights that clinicians rely on.
The paper tackles the problem of limited interpretability in deep learning models for parasite detection in blood smear images by introducing MorphXAI, a framework that unifies detection with fine-grained morphological analysis, resulting in improved detection performance and structured explanations.
Parasitic infections remain a pressing global health challenge, particularly in low-resource settings where diagnosis still depends on labor-intensive manual inspection of blood smears and the availability of expert domain knowledge. While deep learning models have shown strong performance in automating parasite detection, their clinical usefulness is constrained by limited interpretability. Existing explainability methods are largely restricted to visual heatmaps or attention maps, which highlight regions of interest but fail to capture the morphological traits that clinicians rely on for diagnosis. In this work, we present MorphXAI, an explainable framework that unifies parasite detection with fine-grained morphological analysis. MorphXAI integrates morphological supervision directly into the prediction pipeline, enabling the model to localize parasites while simultaneously characterizing clinically relevant attributes such as shape, curvature, visible dot count, flagellum presence, and developmental stage. To support this task, we curate a clinician-annotated dataset of three parasite species (Leishmania, Trypanosoma brucei, and Trypanosoma cruzi) with detailed morphological labels, establishing a new benchmark for interpretable parasite analysis. Experimental results show that MorphXAI not only improves detection performance over the baseline but also provides structured, biologically meaningful explanations.