PlasmoStage: A Hierarchical Deep Learning Framework for Plasmodium Parasite Staging in Malaria
Salem, Mostafa Abouzahir, Saad Ghedira, Hosni El Saddik, Abdulmotaleb Yaqub, Mohammad
Salem, Mostafa
Abouzahir, Saad
Ghedira, Hosni
El Saddik, Abdulmotaleb
Yaqub, Mohammad
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Malaria remains one of the most life-threatening infectious diseases worldwide. Recent advancements in deep learning and computer vision have shown significant promise in automating the analysis of medical images, including malaria-infected blood smears. While hierarchical classification, a machine learning approach that organizes classes into a hierarchy from broad categories to specific subtypes, has proven effective in various domains, its application to malaria parasite staging at the single-cell level remains underexplored. In this work, we present PlasmoStage, a novel hierarchical deep learning frame-work for hierarchical malaria parasite staging. Our approach leverages the DinoBloom foundation model, a state-of-the-art self-supervised model for single-cell image analysis in hematology, as a robust feature extractor. By fine-tuning these features using fully connected layers, PlasmoStage surpasses traditional flat classification models and baseline hierarchical methods. Our contributions are threefold: (1) We introduce a biologically inspired hierarchical classification framework that improves diagnostic accuracy and interpretability by aligning with the natural progression of malaria parasites. (2) We demonstrate the efficacy of foundation model-based feature extraction, achieving state-of-the-art performance with minimal fine-tuning. (3) We provide a comprehensive evaluation on publicly available datasets, including ablation studies and benchmarking against existing methods. Experimental results demonstrate that PlasmoStage effectively differentiates between uninfected and infected cells (Accuracy = 98.66%, Fl-score = 98.38%), accurately identifies parasite species-Plasmodium falciparum or Plasmodium vivax (Accuracy = 98.75%, F1-score = 98.99%), and outperforms conventional flat and hierarchical classification approaches in parasite staging (Vivax staging: Accuracy = 85.90%, F1-score = 85.71%; Falciparum staging: Accuracy = 96.92%, F1-score = 96.62%). This w...
Citation
M. Salem, S. Abouzahir, H. Ghedira, A. El Saddik and M. Yaqub, "PlasmoStage: A Hierarchical Deep Learning Framework for Plasmodium Parasite Staging in Malaria," 2025 IEEE Medical Measurements & Applications (MeMeA), Chania, Greece, 2025, pp. 1-6, doi: 10.1109/MeMeA65319.2025.11067957
Source
2025 IEEE Medical Measurements & Applications (MeMeA)
Conference
IEEE International Workshop on Medical Measurement and Applications (MEMEA)
Keywords
Malaria Parasite, Hierarchical Classification, Foundation Model, Deep Learning
Subjects
Source
IEEE International Workshop on Medical Measurement and Applications (MEMEA)
Publisher
IEEE