@Article{SoniaBaeza2023,
author="Sonia Baeza
and Debora Gil
and Ignasi Garcia Olive
and Maite Salcedo Pujantell
and Jordi Deportos
and Carles Sanchez
and Guillermo Torres
and Gloria Moragas
and Antoni Rosell",
title="Correction: A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients",
journal="European Journal of Nuclear Medicine and Molecular Imaging",
year="2023",
volume="10",
number="1",
pages="13",
optkeywords="early diagnosis",
optkeywords="Lung Cancer",
optkeywords="nodule diagnosis",
optkeywords="Radiomics",
optkeywords="Screening",
abstract="This study shows the generation process and the subsequent study of the representation space obtained by extracting GLCM texture features from computer-aided tomography (CT) scans of pulmonary nodules (PN). For this, data from 92 patients from the Germans Trias i Pujol University Hospital were used. The workflow focuses on feature extraction using Pyradiomics and the VGG16 Convolutional Neural Network (CNN). The aim of the study is to assess whether the data obtained have a positive impact on the diagnosis of lung cancer (LC). To design a machine learning (ML) model training method that allows generalization, we train SVM and neural network (NN) models, evaluating diagnosis performance using metrics defined at slice and nodule level.",
optnote="IAM",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3858), last updated on Mon, 08 Jan 2024 15:33:54 +0100",
doi="10.1186/s40658-023-00532-z",
opturl="https://ejnmmiphys.springeropen.com/articles/10.1186/s40658-023-00532-z"
}