Ponencia
Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI
Autor/es | Amaya Rodríguez, Isabel
Durán López, Lourdes Luna Perejón, Francisco Civit Masot, Javier Domínguez Morales, Juan Pedro Vicente Díaz, Saturnino Civit Balcells, Antón Cascado Caballero, Daniel Linares Barranco, Alejandro |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2019 |
Fecha de depósito | 2019-12-18 |
Publicado en |
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ISBN/ISSN | 978-989-758-384-1 |
Resumen | Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor.
Typically, it is detected through MRI and then either a treatment is applied, or it is removed through ... Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor. Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery. Deep-learning techniques are becoming popular in medical applications and image-based diagnosis. Convolutional Neural Networks are the preferred architecture for object detection and classification in images. In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using Fuzzy C-Means. |
Identificador del proyecto | TEC2016-77785-P |
Cita | Amaya Rodríguez, I., Durán López, L., Luna Perejón, F., Civit Masot, J., Domínguez Morales, J.P., Vicente Díaz, S.,...,Linares Barranco, A. (2019). Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI. En UCCI 2019: 11th International Joint Conference on Computational Intelligence (528-535), Vienna Austria: ScitePress Digital Library. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Glioma Diagnosis Aid.pdf | 930.2Kb | [PDF] | Ver/ | |