Abstract: Objective To construct a fecal microscopy image dataset of intestinal parasite eggs and establish a corresponding deep learning image recognition model, so as to provide technical support for the auxiliary diagnosis of intestinal parasitic diseases. Methods Microscopic images of 12 intestinal parasite eggs were collected using a microscope and a digital camera, pre-processed and labelled with the categories and locations of the eggs to form an image dataset. A masked region convolutional neural network deep learning model was used as a framework to train the bounding box regression, classification, and mask generation, and its performance was evaluated. Results The fecal microscopy image dataset was constructed with a total of 6 299 images, including 10 944 egg images. After testing, the deep learning model achieved an overall recognition accuracy of 90.20%. The recognition accuracy for the 12 types of eggs ranged from 58.65% （Spirometra mansoni egg） to 100.00% （Pinworm egg）. Conclusion Constructing a microscopy image dataset of intestinal parasite eggs and establishing an image recognition model using convolutional neural networks provide technical support for the auxiliary diagnosis of parasitic diseases.

Key words: Image dataset, Intestinal parasites, Intestinal parasite eggs, Deep learning