DISSERTAÇÃO DE MESTRADO - PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO

URI Permanente para esta coleçãohttps://tedebc-teste.ufma.br/handle/tede/1314

Áreas de Concentração e Linhas de Pesquisa: Automação e Contrôle Ciência da Computação Sistemas de Energia Elétrica

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Navegando DISSERTAÇÃO DE MESTRADO - PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO por Assunto "Cancerologia"

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    Aprendizagem Profunda Aplicada ao Diagnóstico de Melanoma
    (Universidade Federal do Maranhão, 2019-02-14) MAIA, Lucas Bezerra; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; BRAZ JÚNIOR, Geraldo; 000520303-18; http://lattes.cnpq.br/8287861610873629; BRAZ JÚNIOR, Geraldo; 000520303-18; http://lattes.cnpq.br/8287861610873629; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; ALMEIDA, João Dallyson Sousa de; http://lattes.cnpq.br/6047330108382641; CARVALHO FILHO, Antonio Oseas de; http://lattes.cnpq.br/7913655222849728
    Melanoma is the most lethal type of cancer when compared to others skin diseases. However, when the diagnosis is made in its initial stage, patients have high rates of recovery. Several approaches to automatic detection and diagnosis of melanoma have been explored by different authors in order to provide an auxiliary opinion to specialists. Training models with the existing data sets have been a difficult task due to the problem of imbalanced data. This work aims to evaluate to the evaluation the performance of machine learning algorithms combined with imbalanced learning technique, regarding the task of melanoma diagnosis. The architectures of Convolutional Neural Networks VGG16, VGG19, Inception, and ResNet were used along with ABCD rule to extract patterns of skin lesions in a set of 200 dermatoscopic images. The Random Forest classifier reached a sensitivity of 92.5 % and a kappa index of 77.15 % after the use of attribute selection with Greedy Stepwise and balancing the training data with Synthetically Minority Oversampling TEchnique (SMOTE) and the Edited Nearest Neighbor (ENN) rule.
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    Segmentação automática da próstata em imagens de ressonância magnética utilizando redes neurais convolucionais, mapa probabilístico e treinamento adversário
    (Universidade Federal do Maranhão, 2019-02-15) FERREIRA, Jonnison Lima; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; SILVA, Aristófanes Corrêa; 288745363-72; http://lattes.cnpq.br/2446301582459104; SILVA, Aristófanes Corrêa; 288745363-72; http://lattes.cnpq.br/2446301582459104; PAIVA, Anselmo Cardoso de; 375523843-87; http://lattes.cnpq.br/6446831084215512; CAVALCANTE, André Borges; http://lattes.cnpq.br/3885279033465023; BRAZ JÚNIOR, Geraldo; http://lattes.cnpq.br/8287861610873629; CARVALHO FILHO, Antonio Oseas de; http://lattes.cnpq.br/7913655222849728
    Prostate cancer is the second most common cancer among men, being the second most deadly. Early detection is a strategy to find the tumor at an early stage and thus provide a better chance of treatment. Currently the prostate gland imaging test has grown for prevention, diagnosis and treatment. The manual segmentation of the prostate is delayed and the propensity to variability among those expected, due to work, alternatives such as computational systems that use image processing and the identification of more advanced and exploited patterns for the early diagnosis of this disease, providing a second opinion for the specialist and increase the process. In this work, several automatic tasks are provided for the segmentation of the prostate from magnetic resonance imaging using a deep learning technique, probabilistic mapping and adversarial training of neural networks. The proposed methodology was tested on two public imaging databases, the Prostate 3T prostate and the PROMISE12, resulting in an average Dice of 89%.