Detecting soil-transmitted helminth and Schistosoma mansoni eggs in Kato-Katz stool smear microscopy images: A comprehensive in- and out-of-distribution evaluation of YOLOv7 variants.

Journal: PLoS neglected tropical diseases
Published Date: Jul 3, 2025

Abstract

BACKGROUND: Soil-transmitted helminth (STH) and Schistosoma mansoni (S. mansoni) infections remain significant public health concerns in tropical and subtropical regions. Deep Convolutional Neural Networks (DCNNs) have already shown promising accuracy in identifying STH and S. mansoni eggs in the same, in-distribution (ID) settings. However, their performance in real-world, out-of-distribution (OOD) scenarios, characterized by variations in image capture devices and the appearance of previously unseen egg types, has not been thoroughly investigated. Assessing the robustness of DCNNs under these challenging conditions is crucial for ensuring their reliability in field diagnostics.

Authors

  • Mohammed Aliy Mohammed
    Jimma Institute of Technology, School of Biomedical Engineering, Jimma University, P.O. Box 378, Jimma, Ethiopia.
  • Esla Timothy Anzaku
    Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
  • Peter Kenneth Ward
    IDLab, Ghent University - imec, Ghent University, Ghent, Belgium.
  • Bruno Levecke
    Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium.
  • Janarthanan Krishnamoorthy
    School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia-378. Electronic address: jana.jk2006@gmail.com.
  • Wesley De Neve
    Center for Biotech Data Science, Department of Environmental Technology, Food Technology and Molecular Biotechnology, Ghent University Global Campus, Songdo, Incheon, South Korea.
  • Sofie Van Hoecke

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