About: Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
Creator	Kim, C Jeon, S
source	ArXiv
abstract	The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world and it is affecting the whole society. The current gold standard test for screening COVID-19 patients is the polymerase chain reaction test. However, the COVID-19 test kits are not widely available and time-consuming. Thus, as an alternative, chest X-rays are being considered for quick screening. Since the presentation of COVID-19 in chest X-rays is varied in features and specialization in reading COVID-19 chest X-rays are required thus limiting its use for diagnosis. To address this challenge of reading chest X-rays by radiologists quickly, we present a multi-channel transfer learning model based on ResNet architecture to facilitate the diagnosis of COVID-19 chest X-ray. Three ResNet-based models (Models a, b, and c) were retrained using Dataset_A (1579 normal and 4429 diseased), Dataset_B (4245 pneumonia and 1763 non-pneumonia), and Dataset_C (184 COVID-19 and 5824 Non-COVID19), respectively, to classify (a) normal or diseased, (b) pneumonia or non-pneumonia, and (c) COVID-19 or non-COVID19. Finally, these three models were ensembled and fine-tuned using Dataset_D (1579 normal, 4245 pneumonia, and 184 COVID-19) to classify normal, pneumonia, and COVID-19 cases. Our results show that the ensemble model is more accurate than the single ResNet model, which is also re-trained using Dataset_D as it extracts more relevant semantic features for each class. Our approach provides a precision of 94 % and a recall of 100%. Thus, our method could potentially help clinicians in screening patients for COVID-19, thus facilitating immediate triaging and treatment for better outcomes.
has issue date	2020-05-12(xsd:dateTime)
has license	arxiv
sha1sum (hex)	10635018a1cc032cba39d259d86b6e7871d71f83
resource representing a document's title	Multi-Channel Transfer Learning of Chest X-ray Images for Screening of COVID-19
resource representing a document's body	covid:10635018a1cc032cba39d259d86b6e7871d71f83#body_text
is schema:about of	named entity 'classify' named entity 'screening' named entity 'facilitating' covid:arg/10635018a1cc032cba39d259d86b6e7871d71f83 named entity 'accurate' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software