Abstract

Enterobius vermicularis (pinworm) infections are a significant global health issue, affecting children predominantly in environments like schools and daycares. Traditional diagnosis using the scotch tape technique involves examining E. vermicularis eggs under a microscope. This method is time-consuming and depends heavily on the examiner’s expertise. To improve this, convolutional neural networks (CNNs) have been used to automate the detection of pinworm eggs from microscopic images. In our study, we enhanced E. vermicularis egg detection using a CNN benchmarked against leading models. We digitized and augmented 40,000 images of E. vermicularis eggs (class 1) and artifacts (class 0) for comprehensive training, using an 80:20 training–validation and a five-fold cross-validation. The proposed CNN model showed limited initial performance but achieved 90.0% accuracy, precision, recall, and F1-score after data augmentation. It also demonstrated improved stability with an ROC-AUC metric increase from 0.77 to 0.97. Despite its smaller file size, our CNN model performed comparably to larger models. Notably, the Xception model achieved 99.0% accuracy, precision, recall, and F1-score. These findings highlight the effectiveness of data augmentation and advanced CNN architectures in improving diagnostic accuracy and efficiency for E. vermicularis infections.