%0 Journal Article
%T CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation
%A Giuseppe Pezzano
%A Oliver Diaz
%A Vicent Ribas Ripoll
%A Petia Radeva
%J Computers in Biology and Medicine
%D 2021
%V 136
%F Giuseppe Pezzano2021
%O MILAB; no menciona
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3635), last updated on Tue, 25 Jan 2022 15:27:27 +0100
%X 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.
%U https://pubmed.ncbi.nlm.nih.gov/34364263/
%U http://dx.doi.org/10.1016/j.compbiomed.2021.104689
%P 104689