© 2014 IEEE. According to the World Health Organization (WHO), as of 2012 esophageal cancer is the eighth-most common cancer globally with 456,000 new cases during the year. One of the triggers of the esophageal cancer is the hiatus hernia, and currently, the frequency disease increases with age, from 10% in patients younger than 40 years to 70% in patients older than 70 years old. Given the above, the aim of this paper is to provide a support tool in the presumptive diagnosis of the hiatus hernia. In the proposed approach we have used two kind of descriptors to provide hiatus hernia's presumptive diagnosis: a first one based on color spaces (RGB, HSV, CIELab and CIELuv) and a second one based on texture descriptors (Local Binary Pattern Histograms). During the experiments, we have tested two kinds of classifiers: K Nearest Neighbor and Random Forest, on a corpus of 48 real cases of images of healthy patients and patients suffering from hiatus hernia. The results are promising, achieving 83% accuracy in classifying the disease.
|Título traducido de la contribución||Una propuesta basada en descriptores de color e histograma de patrones binarios locales como herramienta de apoyo en el diagnóstico presuntivo de hernia de hiato.|
|Estado||Publicada - 9 feb 2014|
|Evento||2014 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2014) - Ixtapa, México|
Duración: 5 nov 2014 → 7 nov 2014
|Conferencia||2014 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2014)|
|Título abreviado||ROPEC 2014|
|Período||5/11/14 → 7/11/14|