Performance assessment of capsule network on different application scenarios
Ver/
Identificadores
URI: http://hdl.handle.net/10317/8105Compartir
Métricas
Estadísticas
Ver Estadísticas de usoMetadatos
Mostrar el registro completo del ítemAutor
Oliva Aparicio, AntonioDirector/a
Larrey Ruiz, JorgeEscuela/Centro
Escuela Técnica Superior de Ingeniería de TelecomunicaciónUniversidad
Universidad Politécnica de CartagenaDepartamento
Tecnologías de la Información y las ComunicacionesÁrea de conocimiento
Teoría de la Señal y las ComunicacionesFecha de publicación
2019-09Palabras clave
GeometríaGeometry
Tecnología médica
Medical technology
Resumen
[ENG]Capsule networks are newborn deep neural networks that substitute traditional artificial neurons by vectors of them called ‘capsules’. This new entities are thought to allow systems to understand the instantiation parameters of objects and induce the construction of an abstracted geometry from them. Thus, capsules enable to achieve a deeper knowledge of the object whose extrapolation procures a further generalization. These new architectures have been compared to the traditional Convolutional Neural Networks (CNNs) on four different scenarios to study their performance. Three of this scenarios are the commonly used datasets: MNIST, CIFAR-10 and SmallNORB; while the other is a set of retinographies containing non-pathological cases and others presenting glaucoma. The goal is to determine whether any of these networks is viable for an early detection of glaucoma. In addition, there is a discussion about the capacity of the structures based on capsules considered to understand and ...
Colecciones
El ítem tiene asociados los siguientes ficheros de licencia:
Redes sociales