Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images

Scientific Reports, vol. 15, pp. 21495

Abstract

In this work, we developed an automated system for the detection and classification of soil-transmitted helminths (STH) and Schistosoma (S.) mansoni eggs in microscopic images of fecal smears. We assembled an STH and S. mansoni dataset comprising over 3,000 field-of-view (FOV) images containing parasite eggs, extracted from more than 300 fecal smear prepared using the Kato-Katz technique. These images were acquired using Schistoscope-a cost-effective automated digital microscope. After annotating the STH and S. mansoni eggs, we employed a transfer learning approach to train an EfficientDet deep learning model, using 70% of the dataset for training, 20% for validation, and 10% for testing. The developed model successfully identified STH and S. mansoni eggs in the FOV images, achieving weighted average scores of [Formula: see text] Precision, [Formula: see text] Sensitivity, [Formula: see text] Specificity, and [Formula: see text] F-Score across four classes of helminths (A. lumbricoides, T. trichiura, hookworm, and S. mansoni). Our system highlights the potential of the Schistoscope, enhanced with artificial intelligence, for detecting STH and S. mansoni infections in remote, resource-limited settings and for supporting the monitoring and evaluation of neglected tropical disease (NTD) control programs.

Authors 7

1. Prosper Oyibo

   Cardiff University · Delft University of Technology

   Affiliation as printed

   Delft Center for Systems and Control, Delft University of Technology, 2628 CN, Delft, The Netherlands

   School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK

2. Brice Meulah Aachen

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Leiden University Center for Infectious Diseases, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands

3. Tope Agbana

   Delft University of Technology

   Affiliation as printed

   Delft Center for Systems and Control, Delft University of Technology, 2628 CN, Delft, The Netherlands

4. Lisette van Lieshout Aachen

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Leiden University Center for Infectious Diseases, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands

5. Wellington Aghoghovwia Oyibo

   University of Lagos

   Affiliation as printed

   Centre for Trans-disciplinary Research for Malaria & Neglected Tropical Diseases, College of Medicine, University of Lagos, Lagos, Nigeria

6. Gleb V. Vdovin

   Delft University of Technology

   Affiliation as printed

   Delft Center for Systems and Control, Delft University of Technology, 2628 CN, Delft, The Netherlands

7. Jan Carel Diehl corresponding

   Delft University of Technology

   Affiliation as printed

   Faculty of Industrial Design Engineering, Delft University of Technology, 2628 CE, Delft, The Netherlands. J.C.Diehl@tudelft.nl