Toward automating the diagnosis of gastrointestinal parasites in cats and dogs

Author(s):Show less -

Joao, L. M. ; Proenca, Leticia Rodrigues ; Loiola, Saulo Hudson Nery ; Inacio, Sandra Valeria ; dos Santos, Bianca Martins ; Rosa, Stefany Laryssa ; Soares, Felipe Augusto ; Stefano, Vitoria Castilho ; Osaku, Daniel ; Suzuki, Celso Tetsuo Nagase ; Bresciani, Katia Denise Saraiva ; Gomes, Jancarlo Ferreira ; Falcao, Alexandre Xavier

• Document type: Journal article
• Source: COMPUTERS IN BIOLOGY AND MEDICINE; v. 163, p. 10-pg., 2023-07-10.

Abstract

Diagnosing gastrointestinal parasites by microscopy slide examination often leads to human interpretation errors, which may occur due to fatigue, lack of training and infrastructure, presence of artifacts (e.g., various types of cells, algae, yeasts), and other reasons. We have investigated the stages in automating the process to cope with the interpretation errors. This work presents advances in two stages focused on gastrointestinal parasites of cats and dogs: a new parasitological processing technique, named TF-Test VetPet, and a microscopy image analysis pipeline based on deep learning methods. TF-Test VetPet improves image quality by reducing cluttering (i.e., eliminating artifacts), which favors automated image analysis. The proposed pipeline can identify three species of parasites in cats and five in dogs, distinguishing them from fecal impurities with an average accuracy of 98,6%. We also make available the two datasets with images of parasites of dogs and cats, which were obtained by processing fecal smears with temporary staining using TF-Test VetPet. (AU)

• FAPESP's process: 17/14189-9 - Automated diagnosis of gastrointestinal parasite images of dogs and cats
• Grantee: Sandra Valéria Inácio
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants