Diagnóstico de nódulos pulmonares em imagens de tomografia computadorizada usando redes neurais convolucionais evolutivas

• Autor(a) principal: SILVA, Giovanni Lucca França da
• Data de Publicação: 2017
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFMA
• Texto Completo: http://tedebc.ufma.br:8080/jspui/handle/tede/1534
• topic: Imagens médicas; Câncer pulmonar; Aprendizagem profunda; Rede neural convolucional; Algoritmo genético; Medical images; Lung cancer; Deep learning; Convolutional neural network; Genetic algorithm; Processamento Gráfico; Cancerologia

Lung cancer is the leading cause of cancer death worldwide, which accounts for more than 17% percent of the total cancer related deaths. However, its early detection may help in a sharp drop in this mortality rate. Because of the arduous analysis process, alternatives such as computational tools that use image processing techniques and pattern recognition have been widely developed and explored for the early diagnosis of this disease, providing a second opinion to the specialist and making this process faster. Therefore, this work proposes a methodology for the diagnosis of slice-based lung nodules extracted from computed tomography images using evolutionary convolutional neural networks. Firstly, the nodules are divided into two sub-regions using the Otsu algorithm based on the particle swarm optimization algorithm. Then, the slices of the nodules and the slices of their sub-regions were resized to the 28 x 28 dimension and given as input simultaneously to the networks. The architecture of the model was composed of three convolutional neural networks sharing the same fully connected layer at the end. Being a parameterized model, the genetic algorithm was applied to optimize some parameters, such as the number of filters in the convolution layers and the number of neurons in the hidden layer. The proposed methodology was tested on the Lung Image Database Consortium and the Image Database Resource Initiative, resulting in a sensitivity of 94.66 %, specificity of 95.14 %, accuracy of 94.78 % and area under the ROC curve of 0.949.