Optimum-Path Forest Applied for Breast Masses Classification

Autor(es)

Ribeiro, Patricia B. ; da Costa, Kelton A. P. ; Papa, Joao P. ; Romero, Roseli A. F. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2014 IEEE 27TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS); v. N/A, p. 4-pg., 2014-01-01.

Resumo

In Computer-Aided Diagnosis-based schemes in mammography analysis each module is interconnected, which directly affects the system operation as a whole. The identification of mammograms with and without masses is highly needed to reduce the false positive rates regarding the automatic selection of regions of interest for further image segmentation. This study aims to evaluate the performance of three techniques in classifying regions of interest as containing masses or without masses (without clinical findings), as well as the main contribution of this work is to introduce the Optimum-Path Forest (OPF) classifier in this context, which has never been done so far. Thus, we have compared OPF against with two sorts of neural networks in a private dataset composed by 120 images: Radial Basis Function and Multilayer Perceptron (MLP). Texture features have been used for such purpose, and the experiments have demonstrated that MLP networks have been slightly better than OPF, but the former is much faster, which can be a suitable tool for real-time recognition systems. (AU)

• Processo FAPESP: 13/20387-7 - Otimização de hiperparâmetros em arquiteturas de aprendizado em profundidade
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Bolsas no Exterior - Pesquisa
• Processo FAPESP: 09/16206-1 - Novas tendências em reconhecimento de padrões baseado em floresta de caminhos ótimos
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores
• Processo FAPESP: 11/14094-1 - Explorando Abordagens de Múltiplos Rótulos por Floresta de Caminhos Ótimos
• Beneficiário: Luis Augusto Martins Pereira
• Modalidade de apoio: Bolsas no Brasil - Mestrado