Impact of SVM Multiclass Decomposition Rules for Recognition of Cancer in Gastroenterology Images

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

Ricardo Sousa (Autor) Outra; Mario Dinis Ribeiro (Autor) Outra; Pedro Pimentel Nunes (Autor) Outra; Miguel Tavares Coimbra (Autor) FCUP

Ata de Conferência Internacional

Título: 2013 IEEE 26TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Páginas: 405-408

26th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

Porto, PORTUGAL, JUN 20-22, 2013

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-008-HZB

DOI: 10.1109/cbms.2013.6627827

Abstract (EN): In this work we study the impact of a set of bag-of-features strategies for the recognition of cancer in gastroenterology images. By using the SIFT descriptor, we analyzed the importance and performance impact of term weighting functions for the construction of visual vocabularies. Further analyzes were conducted in order to ascertain the robustness of multiclass decomposition rules for Support Vector Machines with different kernels. Our study was extended by tailoring a decomposition rule that explores prior knowledge according the four grades of the Singh taxonomy (SDR). We found that SDR coupled with a frequency term weight function attained the best overall results (80%) when trained with an intersection kernel. It also outperformed standard decomposition rules when using a chi(2) kernel and attained competitive performances with a linear kernel.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4