Toward automating the diagnosis of gastrointestinal parasites in cats and dogs

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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

• Tipo de documento: Artigo Científico
• Fonte: COMPUTERS IN BIOLOGY AND MEDICINE; v. 163, p. 10-pg., 2023-07-10.

Resumo

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)

• Processo FAPESP: 17/14189-9 - Diagnóstico automatizado por imagens de parasitos gastrintestinais de cães e gatos
• Beneficiário: Sandra Valéria Inácio
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático