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Joao LM, Proença LR, Loiola SHN, Inácio SV, Dos Santos BM, Rosa SL, Soares FA, Stefano VC, Osaku D, Suzuki CTN, Bresciani KDS, Gomes JF, Falcão AX. Toward automating the diagnosis of gastrointestinal parasites in cats and dogs. Comput Biol Med 2023; 163:107203. [PMID: 37437360 DOI: 10.1016/j.compbiomed.2023.107203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/13/2023] [Accepted: 06/25/2023] [Indexed: 07/14/2023]
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.
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Affiliation(s)
- L M Joao
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
| | - Letícia Rodrigues Proença
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Saulo Hudson Nery Loiola
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Sandra Valéria Inácio
- School of Veterinary Medicine, São Paulo State University (UNESP), R. Clóvis Pestana, Araçatuba, 16050-680, São Paulo, Brazil.
| | - Bianca Martins Dos Santos
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Stefany Laryssa Rosa
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Felipe Augusto Soares
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Vitória Castilho Stefano
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
| | - Daniel Osaku
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
| | - Celso Tetsuo Nagase Suzuki
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
| | - Katia Denise Saraiva Bresciani
- School of Veterinary Medicine, São Paulo State University (UNESP), R. Clóvis Pestana, Araçatuba, 16050-680, São Paulo, Brazil.
| | - Jancarlo Ferreira Gomes
- School of Medical Sciences, State University of Campinas, R. Tessália Vieira de Camargo, Campinas, 13083-887, São Paulo, Brazil.
| | - Alexandre Xavier Falcão
- Institute of Computing, State University of Campinas, R. Saturnino de Brito, Campinas, 13083-852, São Paulo, Brazil.
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