PathoSpotter: um sistema para classificação de glomerulopatias a partir de imagens histológicas renais

Detalhes bibliográficos
Ano de defesa: 2016
Autor(a) principal: Barros, George Oliveira lattes
Orientador(a): Duarte, Angelo Amâncio
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Estadual de Feira de Santana
Programa de Pós-Graduação: Mestrado em Computação Aplicada
Departamento: DEPARTAMENTO DE TECNOLOGIA
País: Brasil
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: http://localhost:8080/tede/handle/tede/389
Resumo: The realization of an accurate diagnosis from histological images requires pathologists with practical experience because the characteristics of these images lead to a subjective analysis, which often hamper the accuracy of diagnosis. Systems that help to achieve better diagnoses can minimize doubts and improve the quality of diagnosis, influencing on increasing the effectiveness of medical treatments. This paper describes the research and development of PathoSpotter, a computer system to aid in the identification of diseases from histological images. The PathoSpotter proposes to reduce the lack of support work to histopathological diagnosis of renal diseases since much has been done in the area of cancer, but there is few published material in relation to the Digital Pathology applied to nephrology and hepatology. Our goal in this study was to apply the PathoSpotter the classification of proliferative glomerulopathy, which is a family of primary diseases affecting the kidneys. The work was based on a data set consisting of 811 histological pictures glomeruli and classical techniques of processing digital images and histopathology were used. The PathoSpotter presented a performance of 88.4% accuracy, which was similar to other Digital Pathology jobs that can be found in the literature.