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

• Ano de defesa: 2016
• Autor(a) principal: Barros, George Oliveira
• Orientador(a): Duarte, Angelo Amâncio
• 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: Glomerulopatias; Processamento digital de imagens; Aprendizado de máquina; Histopatologia digital
• Palavras-chave em Inglês: Glomerulopathy; Digital image processing; Machine learning; Digital histopathology
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO
• Link de acesso: http://localhost:8080/tede/handle/tede/389

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.