MorphXAI: An Explainable Framework for Morphological Analysis of Parasites in Blood Smear Images

TL;DR

MorphXAI is an explainable AI framework that combines parasite detection with detailed morphological analysis in blood smear images, aiding diagnosis and interpretability in parasitology.

Contribution

It introduces a novel integrated approach that unifies detection and morphological characterization, supported by a new annotated dataset for parasite analysis.

Findings

Improves parasite detection accuracy

Provides structured morphological explanations

Establishes a new benchmark dataset

Abstract

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.