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Constructing a Predictive Model for STH and Schistosomiasis Classification From Microscopic Images.

Etefa Belachew1, Kris Calpotura1, Abrham Adamu2

  • 1Faculty of Electrical and Computer Engineering, Jimma University-Institute of Technology, Jimma, Ethiopia.

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Summary

A new hybrid machine learning and deep learning system accurately diagnoses parasitic infections from microscope images, offering a faster, more precise alternative for resource-limited regions. This AI-driven approach improves upon traditional methods for soil-transmitted helminths and schistosomiasis detection.

Keywords:
STH and schistosomiasisViTdeep learningdigital image processingmachine learningpretrained models

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Area of Science:

  • Parasitology
  • Medical Diagnostics
  • Artificial Intelligence

Background:

  • Soil-transmitted helminths (STHs) and schistosomiasis cause significant health and economic burdens in tropical regions, especially Africa.
  • Traditional microscopy for diagnosing these parasitic diseases is slow, labor-intensive, and challenging in resource-limited settings like Ethiopia.
  • Accurate and rapid diagnostics are crucial for effective disease control and treatment strategies.

Purpose of the Study:

  • To develop and evaluate an innovative system combining machine learning (ML) and deep learning (DL) for the rapid and accurate analysis of parasite egg images.
  • To compare the diagnostic performance of a hybrid CNN-ML model against standalone deep learning models and vision transformers (ViTs).
  • To assess the system's utility for classifying five categories: Ascaris, hookworm, schistosomiasis, Trichuris, and negative samples.

Main Methods:

  • A dataset of 1490 parasite egg images from Ethiopia was utilized, undergoing preprocessing including resizing, normalization, and augmentation.
  • Convolutional Neural Network (CNN) architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB0) and Vision Transformers (ViTs) were employed as feature extractors.
  • Machine learning classifiers (SVM, XGBoost, KNN, RF, DT) were used for final predictions in the hybrid model.

Main Results:

  • The hybrid CNN-ML model significantly outperformed standalone deep learning models.
  • VGG16-SVM and VGG16-XGBoost achieved the highest test accuracies at 99.31% and 99.35%, respectively.
  • Standalone CNNs demonstrated lower accuracies (e.g., VGG16: 79.98%, DenseNet121: 84.12%), highlighting the hybrid approach's superiority.

Conclusions:

  • The developed hybrid CNN-ML system offers enhanced diagnostic speed and accuracy for parasitic infections compared to traditional methods.
  • This AI-powered diagnostic tool has the potential to significantly improve disease management in resource-limited settings by enabling real-time analysis.
  • Further research is needed to address limitations such as dataset size, diversity, and potential image degradation for improved generalizability.