Dissertation/ Thesis
Identificação e quantificação automática de taxa de glomérulos hialinizados utilizando deep learning ; Identification and quantification of hyalinized glomerulus rate using deep learning
| العنوان: | Identificação e quantificação automática de taxa de glomérulos hialinizados utilizando deep learning ; Identification and quantification of hyalinized glomerulus rate using deep learning |
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| المؤلفون: | Costa, Thalita Munique |
| المساهمون: | Schneider, Fabio Kurt, https://orcid.org/ 0000-0001-6916-1361, http://lattes.cnpq.br/1463591813823167, Silva, Wilson Jose da, orcid:0000-0002-6288-3625, http://lattes.cnpq.br/6419561860187332, Paula Filho, Pedro Luiz de, https://orcid.org/ 0000-0002-6291-9237, http://lattes.cnpq.br/8149364045680042, Ioshii, Sergio Ossamu, https://orcid.org/ 0000-0002-7871-4463, http://lattes.cnpq.br/0515201301625481 |
| بيانات النشر: | Universidade Tecnológica Federal do Paraná Curitiba Brasil Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial UTFPR |
| سنة النشر: | 2020 |
| المجموعة: | Universidade Tecnológica Federal do Paraná (UTFPR): Repositório Institucional (RIUT) |
| مصطلحات موضوعية: | Glomérulos renais - Detecção, Aprendizado de máquinas, Processamento de imagens - Técnicas digitais, Histopatologia, Redes neurais (Computação), Simulação (Computadores), Kidney glomerulus - Detection, Machine learning, Image processing - Digital techniques, Histology, Pathological, Neural networks (Computer science), Computer simulation, CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA, Engenharia Elétrica |
| الوصف: | In Digital Pathology, histological slides are scanned for further analysis. Digitized slides allow the use of artificial intelligence and image processing techniques for automatic identification and quantification in histopathology, allowing the quantification of the rate of hyalinized glomeruli. In this work, a database with images from several renal pathology study centers is utilized and the usage of Deep Learning, specifically the YOLOV3 architecture, is evaluated in the automatic detection of glomeruli. In addition to the assessment of functional glomeruli, there is also a need to identify the presence and percentage of hyalinized glomeruli (i.e. glomeruli that have become non-functional due to the replacement of all histopathological elements with hyaline protein material), when considering the totality of existing glomeruli. Using the Bio Atlas database (Pennsylvania State University), 16 complete slides, which resulted in 1177 images of 1024x1024 pixels with one or more glomeruli, were used for training and validation. A total of 468 hyalinized glomeruli and 1261 non-hyalinized glomeruli were noted. Using the 53-layer convolutional neural network and input images adjusted to 512x512 pixels, this work obtained a sensitivity of 90%, precision of 96.9%, accuracy of 87.5% and an F1 score of 93.3% considering the two types of glomeruli. A system was created to identify functional and hyalinized glomeruli, allowing support for histopathological study of kidney diseases and facilitating the location of the objects of analysis. ; Em Patologia Digital, lâminas histológicas são digitalizadas para posterior análise. Lâminas digitalizadas permitem o uso de técnicas de inteligência artificial e processamento de imagens para identificação e quantificação automática em histopatologia permitindo a quantificação da taxa de glomérulos hialinizados. Neste trabalho, uma base de dados com imagens de vários centros de estudos de patologia renal é utilizada e o uso de Deep Learning, especificamente a arquitetura YOLOV3, é ... |
| نوع الوثيقة: | master thesis |
| وصف الملف: | application/pdf |
| اللغة: | Portuguese |
| Relation: | COSTA, Thalita Munique. Identificação e quantificação automática de taxa de glomérulos hialinizados utilizando deep learning. 2020. Dissertação (Mestrado em Engenharia Elétrica e Informática Industrial) - Universidade Tecnológica Federal do Paraná, Curitiba, 2020.; http://repositorio.utfpr.edu.br/jspui/handle/1/28827 |
| الاتاحة: | http://repositorio.utfpr.edu.br/jspui/handle/1/28827 |
| Rights: | openAccess ; http://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: | edsbas.7AEDC927 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |