About: Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices

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

About: Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices
Creator	Dey, Nilanjan Ahuja, Sakshi Bijaya, · Gandhi, Kumar Panigrahi, Ketan Rajinikanth, · Tapan, ·
Source	PMC
abstract	Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pandemic disease. The COVID-19 disease has adverse effects on the respiratory system, and the infection severity can be detected using a chosen imaging modality. In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures. The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy (training = 99.82%, validation = 97.32%, and testing = 99.4%) on the considered image dataset compared with the alternatives.
has issue date	2020-08-21(xsd:dateTime)
bibo:doi	10.1007/s10489-020-01826-w
has license	no-cc
sha1sum (hex)	2af6283bd82df96685005957e90be8d9270d6dd6
schema:url	https://doi.org/10.1007/s10489-020-01826-w
resource representing a document's title	Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices
has PubMed Central identifier	PMC7440966
schema:publication	Appl Intell
resource representing a document's body	covid:2af6283bd82df96685005957e90be8d9270d6dd6#body_text
is schema:about of	named entity 'detection' named entity 'infection' named entity 'training' named entity 'performance measures' named entity 'testing' named entity 'COMPARED' named entity 'AGE GROUP' named entity 'VALIDATION' named entity 'TRAINED' named entity 'DETECTION' named entity 'COMPUTING' named entity 'chosen' named entity 'experimental' named entity 'globally' named entity 'CT scan' named entity 'Lung' named entity 'three-phase' named entity 'evaluated' named entity 'architectures' named entity 'network' named entity 'transfer learning' named entity 'decomposed' named entity 'images' named entity 'experimental' named entity 'transfer' named entity 'pandemic disease' named entity 'pre-trained' named entity 'wavelet' named entity 'Lung' named entity 'pre-trained' named entity 'CT scan' named entity 'lung' named entity 'COVID-19' named entity 'CT scan' named entity 'image processing' named entity 'ResNet' named entity 'transfer learning' named entity 'COVID' named entity 'deep learning' named entity 'pulmonary hypertension' named entity 'SqueezeNet' named entity 'wavelet' named entity 'transfer learning' named entity 'polymerase chain reaction' named entity 'radiographic' named entity 'data augmentation' named entity 'COVID-19 disease' named entity 'lung' named entity 'infection' named entity 'lungs' named entity 'CNN' named entity 'Neural Network' named entity 'CT scan' named entity 'virus' named entity 'binary classification' named entity 'testing kits' named entity 'COVID' named entity 'vanishing gradient' named entity 'transfer learning' named entity 'transfer learning' named entity 'CT scan' named entity 'binary classification' named entity 'wavelet' named entity 'medical imaging' named entity 'CT scan' named entity 'transfer learning' named entity 'transfer learning' named entity 'COVID' named entity 'CT scan' named entity 'CNN' named entity 'data augmentation' named entity 'pulmonary embolism' named entity 'CT scan' named entity 'AUC' named entity 'neural networks' named entity 'CT scan' named entity 'India'

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 © 2009-2024 OpenLink Software