Browsing by Subject "Data analysis"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Publication
    Open Access
    A ViTUNeT-based model using YOLOv8 for efficient LVNC diagnosis and automatic cleaning of dataset
    (De Gruyter, 2025-06-04) Haro Orenes, Salvador de; Bernabé García, Gregorio; García Carrasco, José Manuel; González Férez, Pilar; Ingeniería y Tecnología de Computadores
    Left ventricular non-compaction is a cardiac condition marked by excessive trabeculae in the left ventricle’s inner wall. Although various methods exist to measure these structures, the medical community still lacks consensus on the best approach. Previously, we developed DL-LVTQ, a tool based on a UNet neural network, to quantify trabeculae in this region. In this study, we expand the dataset to include new patients with Titin cardiomyopathy and healthy individuals with fewer trabeculae, requiring retraining of our models to enhance predictions. We also propose ViTUNeT, a neural network architecture combining U-Net and Vision Transformers to segment the left ventricle more accurately. Additionally, we train a YOLOv8 model to detect the ventricle and integrate it with ViTUNeT model to focus on the region of interest. Results from ViTUNet and YOLOv8 are similar to DL-LVTQ, suggesting dataset quality limits further accuracy improvements. To test this, we analyze MRI images and develop a method using two YOLOv8 models to identify and remove problematic images, leading to better results. Combining YOLOv8 with deep learning networks offers a promising approach for improving cardiac image analysis and segmentation.
  • Thumbnail Image
    Publication
    Embargo
    Application of YOLOv8 and a model based on vision transformers and UNet for LVNC diagnosis: advantages and limitations
    (Springer, 2025-04-25) De Haro, Salvador; González Férez, Pilar; García, José M.; Bernabé García, Gregorio; Ingeniería y Tecnología de Computadores
    Hypertrabeculation or left ventricular non-compaction (LVNC) is a cardiac condition that has recently been recognized. While several methods exist for accurately measuring the trabeculae in the ventricle, there is still no consensus within the medical community regarding the optimal approach. In previous work, we introduced DL-LVTQ, a tool based on a UNet convolutional neural network designed to quantify the trabeculae in the left ventricle. In this paper, we present an expanded dataset that includes new patients affected by a cardiomyopathy known as Titin, necessitating the retraining of the models involved in our study on this updated dataset to accurately infer future patients with this condition. We also introduce ViTUNet, a hybrid architecture that aims to merge the benefits of UNet and Vision Transformers for precise segmentation of the left ventricle. Furthermore, we train a YOLOv8 model to detect the left ventricle and integrate it with the hybrid model to focus segmentation on a region of interest around the ventricle. Regarding the precision quality achieved by ViTUNet using YOLOv8, results are quite similar to those obtained by the DL-LVTQ tool, suggesting that the dataset is a limiting factor in our improvement. To substantiate this, we conduct a detailed analysis of the MRI slices in the current dataset. By identifying and removing problematic slices, results significantly improve. The introduction of a YOLOv8 model alongside a deep learning model presents a promising approach.
  • Thumbnail Image
    Publication
    Open Access
    Evaluation of ontology structural metrics based on public repository data
    (Oxford University Press, 2019) Franco Nicolás, Manuel; Vivo Molina, Juana María; Quesada Martínez, Manuel; Duque Ramos, Astrid; Fernández Breis, Jesualdo Tomás; Informática y Sistemas
  • Thumbnail Image
    Publication
    Open Access
    Making decisions for frost prediction in agricultural crops in a softcomputing framework
    (ScienceDirect, 2020) Cadenas Figueredo, J.M.; Garrido Carrera, María del Carmen; Martínez España, R.; Guillén-Navarro, M.A.; Ingeniería de la Información y las Comunicaciones
    Nowadays, there are many areas of daily life that can obtain benefit from technological advances and the large amounts of information stored. One of these areas is agriculture, giving place to precision agriculture. Frosts in crops are among the problems that precision agriculture tries to solve because produce great economic losses to farmers. The problem of early detection of frost is a process that involves a large amount of wheather data. However, the use of these data, both for the classification and regression task, must be carried out in an adequate way to obtain an inference with quality. A preprocessing of them is carried out in order to obtain a dataset grouping attributes that refer to the same measure in a single attribute expressed by a fuzzy value. From these fuzzy time series data we must use techniques for data analysis that are capable of manipulating them. Therefore, first a regression technique based on k-nearest neighbors in a Soft Computing framework is proposed that can deal with fuzzy data, and second, this technique and others to classification are used for the early detection of a frost from data obtained from different weather stations in the Region of Murcia (south-east Spain) with the aim of decrease the damages that these frosts can cause in crops. From the models obtained, an interpretation of the provided information is performed and the most relevant set of attributes is obtained for the anticipated prediction of a frost and of the temperature value. Several experiments are carried out on the datasets to obtain the models with the best performance in the prediction validating the results by means of a statistical analysis.
  • Thumbnail Image
    Publication
    Open Access
    El pensamiento computacional en educación. Análisis bibliométrico y temático
    (Universidad de Murcia, Servicio de Publicaciones, 2020) Roig-Vila, Rosabel; Moreno-Isac, Víctor
    El pensamiento computacional se está considerando actualmente como una de las competencias más demandadas y, de ahí, su planteamiento en el contexto educativo. Este trabajo trata de analizar la literatura científica sobre la aplicación del pensamiento computacional en el ámbito educativo publicada en las colecciones principales de la base de datos Web of Science. Para lograrlo, se lleva a cabo una revisión sistemática donde se han tenido en cuenta las variables de año de publicación, los países con más producciones, las autorías más productivas en este campo y fuentes documentales con mayor número de publicaciones. Asimismo, se ha realizado una clasificación según los tipos de documentos y los métodos de investigación utilizados, así como las etapas educativas objeto de estudio y los lenguajes de programación utilizados. Se ha hallado una tendencia creciente de publicaciones en esta temática, donde España es uno de los países donde más se publica. Además, se ha observado cómo este campo de estudio se ha abordado desde los dos principales métodos de investigación –cuantitativo y cualitativo— y la etapa educativa más investigada es la educación primaria