A Semantic Ontology-Driven Explainable Classifier for Identifying Plasmodium Species and Stages in Thin Smear Images
Keywords:
Semantic segmentation, Ontology-based reasoning, Malaria diagnosis automation, XAI, Interpretable Classifiers.Abstract
This study presents an explainable classifier for identifying Plasmodium species and life stages from thin smear images by integrating Convolutional Neural Networks (CNNs) with Pathogen Ontology. Using the CDC Thin Smear dataset, the model applies SegNet for pixel-wise semantic segmentation, identifying features like infected red blood cells, ring forms, and gametocytes. Ontological reasoning maps visual features to structured biological concepts, producing interpretable outputs (e.g., "Parasitized: hasCleft AND hasDots"). This approach enhances diagnostic transparency, enabling clinicians to understand the AI’s decisions. Additionally, Grad-CAM visualizations support explainability by highlighting relevant image regions, fostering trust in the system. The combination of deep learning and ontology ensures real-time, reliable malaria diagnostics, reducing human error while maintaining clinical relevance.
