Dissertação

{en_GB=Application of Deep Learning Techniques to the Diagnosis of Medical Images} {} EVALUATED

{pt=A Retinopatia Diabética (RD) é a maior causa de perca de visão a nível mundial. Apesar de ser facilmente tratável quando diagnosticada atempadamente, existe atualmente a necessidade de métodos mais baratos e eficazes de o fazer. As imagens médicas já são usadas como método de diagnóstico há muito tempo. Avanços recentes na área da visão computacional têm mostrado resultados notáveis através do uso de Convolutional Neural Networks, as quais têm sido capazes de atingir resultados estado-de-arte na segmentação de imagens., en=Diabetic Retinopathy (DR) is the leading cause of visual disability worldwide. Although it is highly treatable when diagnosed in its earlier stages, there is currently a need of cheaper and more accurate ways to do so. Medical images have been used in diagnosis for a long time. Recent advancements in the computer vision field have shown remarkable results through the use of Convolutional Neural Networks, that have been able to reach state-of-the-art results in image segmentation. In this master's thesis, we implemented a V-Net like architecture in Python and study how image preprocessing techniques to highlight lesions associated with DR, and different optimization metrics have an impact on its results. The results show that the impact of this variables changes according to the lesion that we try to segment and that the V-Net is capable of obtaining good results for some of the segmentation problems.}
{pt=Retinopatia Diabética, Visão computacional, Convolutional Neural Networks, Segmentação, V-Net, Pré-Processamento de Imagem, en=Diabetic Retinopathy, Computer Vision, Convolutional Neural Networks, Segmentation, V-Net, Image Preprocessing}

Novembro 6, 2018, 18:0

Publicação

Obra sujeita a Direitos de Autor

Orientação

ORIENTADOR

Arlindo Manuel Limede de Oliveira

Departamento de Engenharia Informática (DEI)

Professor Catedrático