Automated Classification of Parasitic Worm Eggs Based on Transfer Learning and Fine-Tuned CNN Models

Abstract

Classification of worm eggs is important for diagnosing worm diseases, but the manual process is time-consuming. This study designs an image classification system using Convolutional Neural Network (CNN), transfer learning, and fine-tuning. The main goal of this study is to create a CNN model to sort parasitic worm eggs into groups. It does this by comparing three CNN architectures: EfficientNetB0, MobileNetV3, and ResNet50; it also creates classification technology for diagnosing worm infections. We applied transfer learning with pre-trained models and fine-tuned them for the IEEE parasitic egg dataset. The results reveal that EfficientNetB0 is superior, with an accuracy of 95.36%, precision of 95.80%, recall of 95.38%, and F1-score of 95.48%. It performs better and more efficiently than the other two architectures. Applying transfer learning and fine-tuning improves model performance, with EfficientNetB0 consistently outperforming. Furthermore, visual similarities between classes in the dataset likely cause prediction errors. Therefore, this system can support the diagnosis of worm diseases with high efficiency and accuracy.