@Article{GiuseppePezzano2021,
author="Giuseppe Pezzano
and Oliver Diaz
and Vicent Ribas Ripoll
and Petia Radeva",
title="CoLe-CNN+: Context learning -- Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation",
journal="Computers in Biology and Medicine",
year="2021",
volume="136",
pages="104689",
abstract="The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59\% and 71\%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19.",
optnote="MILAB; no menciona",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3635), last updated on Tue, 25 Jan 2022 15:27:27 +0100",
doi="10.1016/j.compbiomed.2021.104689",
opturl="https://pubmed.ncbi.nlm.nih.gov/34364263/"
}