A Robust Ensemble Model for Patasitic Egg Detection and Classification
This work addresses the need for a time-saving and high-sensitivity examination method for intestinal parasitic infections, which are a leading cause of morbidity worldwide, though it appears incremental in applying existing deep learning techniques to this domain.
The paper tackles the problem of detecting and classifying parasitic eggs in microscope images by applying object detectors like YOLOv5 and cascadeRCNNs, achieving excellent performance on a challenge dataset through optimizations such as data augmentation and model ensemble, with added noise training enhancing robustness against polluted inputs.
Intestinal parasitic infections, as a leading causes of morbidity worldwide, still lacks time-saving, high-sensitivity and user-friendly examination method. The development of deep learning technique reveals its broad application potential in biological image. In this paper, we apply several object detectors such as YOLOv5 and variant cascadeRCNNs to automatically discriminate parasitic eggs in microscope images. Through specially-designed optimization including raw data augmentation, model ensemble, transfer learning and test time augmentation, our model achieves excellent performance on challenge dataset. In addition, our model trained with added noise gains a high robustness against polluted input, which further broaden its applicability in practice.