Constructing a Predictive Model for Soil-Transmitted Helminths And Schistosomiasis Classification from Microscopic Images

Abstract

Soil-transmitted helminths and schistosomiasis are widespread parasitic diseases in tropical areas, especially in Africa, causing significant health impacts. Prompt treatment offers both health and economic benefits. Current diagnosis, mainly microscopy-based, is time-intensive and challenging in low-resource settings like Ethiopia. This study develops an innovative sys tem that analyzes parasite egg images from microscopes. Unlike previous CNN-only ap proaches, it combines machine learning and deep learning for faster, more accurate disease identification, enhancing diagnostic efficiency and reliability. This study compared predictive model with standalone deep learning for system modeling, focusing on five classes: ascariasis, hookworms, schistosomiasis, Trichuris, and negative sam ples. The dataset, from the Ethiopian Public Health Institute’s research center, contained 1,490 images (300 per class and 290 for negatives). Various image processing steps resizing, normal ization, and augmentation were applied. Models including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB0, and Vision Transformer served as classifiers and feature ex tractors. Additionally, machine learning classifiers such as XGBoost, SVM, KNN, Random Forest, and Decision Trees were integrated with deep extractors for classifiers. The predictive model demonstrated higher accuracy. Strong results were obtained with SVM, where VGG16 and DenseNet121 as feature extractors led to 99.31% test accuracy. Also VGG and xgboost shows highest test accuracy of 99.35%. However, CNN-only models showed lower accuracy. VGG16 achieved 79.98% test accuracy and 83.4% training accuracy, while DenseNet121 reached 84.12% test and 88.56% training accuracy. ResNet50’s training accuracy was 92.23%, with 86.01% on testing; Ef ficientNetB0 achieved 91.80% training and 84.33% testing accuracy; MobileNetV2 reached 90.49% training and 87.02% test accuracy, and Vision Transformer recorded 93.75% training and 87.43% test accuracy. At class level Negative samples show high accuracy while others show different accuracy based on model types. These applications improve the diagnostic utility as they feed real-time information and are convenient to use in areas where even primary healthcare may not be available. Working with a small and long stored dataset posed challenges due to limited diversity and sample degradation, which hindered accu rate class distinction and affected the model’s generalization performance. To overcome dataset limita tions, collect fresh samples to increase diversity and represent all classes adequately. Implement sys tematic field collection under varied conditions, ensuring data quality. Collaborate with relevant insti tutions or stakeholders to expand the dataset, emphasizing consistency and accuracy.