Automatic identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks.

ABSTRACT: Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome the shortcomings of manual detection performed by experts. Therefore, we propose an approach to identify six captive reptiles parasitic agents (Ophionyssus natricis, Blastocystis sp, Oxiurdo egg, Rhytidoides similis, Strongyloides, Taenia) or the absence of such parasites from a microscope stool images dataset. Towards this end, we first use an image segmentation stage to detect the parasite within the image, which combines the Contrast Limited Ada

SUBMITTER: Parra C 

PROVIDER: S-EPMC9352023 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature