Um método automático de detecção de massas em mamografias por meio de redes neurais

• Ano de defesa: 2012
• Autor(a) principal: Barbosa Filho, José Rogério Bezerra
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraí­ba; BR; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Mamografia; Redes neurais artificiais; Detecção; Massas; Mammography; Computer Aided Detection and Diagnosis - CADDx; Artificial neural networks; Detection; Masses; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/6077

Breast cancer is the most common cause of death by cancer in the female population and a serious world health problem. The mammographic exam allows an early detection which reduces the mortality rate of the disease. Its efficiency has made it the standard procedure for breast cancer diagnosis. These reasons have led to the development of Computer-Aided Detection and Diagnosis (CADDx) systems that assist the physician by working as a second opinion in the diagnostic. One of the algorithms studied during the development of this work, the mass detection algorithm created by Ozekes et al, has shown great potential reaching 99% of sensibility when applied in the test group images. However, its many parameters and the need to manual calibrate them make it impossible to use it in the constructions of practical CADDx systems. This work presents an automatic method for mass detection in mammography based on the algorithm of Ozekes et al. Multilayer Perceptron artificial neural networks (ANN) are used as functional approximators to automatically calibrate the necessary parameters of the proposed method. The computation of the neural networks produces the values used as parameters for thresholding and template application stages. Feature selection and network topologies were chosen by means of empirical tests. Results show in its best configuration point 82% of sensibility and 7,51 false positives per image. After a false positive reduction, 74% of sensibility and 3,56 false positives per image were achieved. Future works include the study of a wider set of image features and preprocessing algorithms.