Segmentação automática de região de interesse em imagens termográficas da mama utilizando redes profundas

• Autor(a) principal: Pinto, Tiago da Silva e Souza
• Data de Publicação: 2023
• Tipo de documento: Trabalho de conclusão de curso
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/39556
• topic: Segmentação de imagens; Image segmentation; Câncer de mama; Breast cancer; Termografia infravermelha; Infrared thermography; Região de interesse; Region of interest; Rede neural convolucional; Convolutional neural network; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Breast cancer is the most incident type of cancer in women in Brazil, with a forecast of over 73 thousand new cases expected for the year 2023. Early detection of this type of cancer significantly increases the chances of successful treatment. In this context, infrared thermography stands out as a non-invasive technique that can be applied even in younger women for breast cancer detection. The analysis of thermographic breast images for breast cancer detection is a topic addressed in the literature, and in this sense, the segmentation of the region of interest in these images aims to remove some information that may be considered as noise for the automatic detection of the disease. This work proposes a method for segmenting thermographic breast images using the DeepLabV3+ Convolutional Neural Network, achieving average accuracies, Intersection over Union, and precision of 98.69%, 97.18%, and 98.48%, respectively.