Classificação de lesões em mamografias por análise de componentes independentes, análise discriminante linear e máquina de vetor de suporte

• Ano de defesa: 2008
• Autor(a) principal: DUARTE, Daniel Duarte
• Orientador(a): BARROS FILHO, Allan Kardec Duailibe
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Máquinas de vetor de suporte; Mamografias; Diagnóstico auxiliado por computador; Análise discriminante linear; Análise de componentes independentes; Análise de componentes principais
• Palavras-chave em Inglês: Principal component analysis; Independent component analysis; Linear discriminant analysis; Computer aided diagnosis; Mammogram; Support vector machine
• Área do conhecimento CNPq: Engenharia Biomédica
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1816

Female breast cancer is the major cause of death in western countries. Efforts in Computer Vision have been made in order to add improve the diagnostic accuracy by radiologists. In this work, we present a methodology that uses independent component analysis (ICA) along with support vector machine (SVM) and linear discriminant analysis (LDA) to distinguish between mass or non-mass and benign or malign tissues from mammograms. As a result, it was found that: LDA reaches 90,11% of accuracy to discriminante between mass or non-mass and 95,38% to discriminate between benign or malignant tissues in DDSM database and in mini-MIAS database we obtained 85% to discriminate between mass or non-mass and 92% of accuracy to discriminate between benign or malignant tissues; SVM reaches 99,55% of accuracy to discriminate between mass or non-mass and the same percentage to discriminate between benign or malignat tissues in DDSM database whereas, and in MIAS database it was obtained 98% to discriminate between mass or non-mass and 100% to discriminate between benign or malignant tissues.