Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

• Ano de defesa: 2015
• Autor(a) principal: Franco, Marcelo Lemos Nunes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Biomédica
• Palavras-chave em Português: Engenharia biomédica; Tomografia; Pulmões - Câncer; Redes neurais artificiais; Câncer Pulmonar; Redes Neurais Artificiais em Cascata; Tomografia Computadorizada; Teste T de Student; Teste U de Mann-Whitney; Lung Cancer; Cascade Artificial Neural Network; Computed Tomography; T Test of Student; U Test of Mann-Whitney; CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/17664; https://doi.org/10.14393/ufu.di.2015.535

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.