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SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos
Lung Ultrasound (LUS) images are considered to be effective for detecting Coronavirus Disease (COVID-19) as an alternative to the existing reverse transcription-polymerase chain reaction (RT-PCR)-based detection scheme. However, the recent literature exhibits a shortage of works dealing with LUS ima...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269407/ https://www.ncbi.nlm.nih.gov/pubmed/34257953 http://dx.doi.org/10.1007/s13755-021-00154-8 |
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author | Sadik, Farhan Dastider, Ankan Ghosh Fattah, Shaikh Anowarul |
author_facet | Sadik, Farhan Dastider, Ankan Ghosh Fattah, Shaikh Anowarul |
author_sort | Sadik, Farhan |
collection | PubMed |
description | Lung Ultrasound (LUS) images are considered to be effective for detecting Coronavirus Disease (COVID-19) as an alternative to the existing reverse transcription-polymerase chain reaction (RT-PCR)-based detection scheme. However, the recent literature exhibits a shortage of works dealing with LUS image-based COVID-19 detection. In this paper, a spectral mask enhancement (SpecMEn) scheme is introduced along with a histogram equalization pre-processing stage to reduce the noise effect in LUS images prior to utilizing them for feature extraction. In order to detect the COVID-19 cases, we propose to utilize the SpecMEn pre-processed LUS images in the deep learning (DL) models (namely the SpecMEn-DL method), which offers a better representation of some characteristics features in LUS images and results in very satisfactory classification performance. The performance of the proposed SpecMEn-DL technique is appraised by implementing some state-of-the-art DL models and comparing the results with related studies. It is found that the use of the SpecMEn scheme in DL techniques offers an average increase in accuracy and [Formula: see text] score of [Formula: see text] and [Formula: see text] , respectively, at the video-level. Comprehensive analysis and visualization of the intermediate steps manifest a very satisfactory detection performance creating a flexible and safe alternative option for the clinicians to get assistance while obtaining the immediate evaluation of the patients. |
format | Online Article Text |
id | pubmed-8269407 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-82694072021-07-09 SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos Sadik, Farhan Dastider, Ankan Ghosh Fattah, Shaikh Anowarul Health Inf Sci Syst Research Lung Ultrasound (LUS) images are considered to be effective for detecting Coronavirus Disease (COVID-19) as an alternative to the existing reverse transcription-polymerase chain reaction (RT-PCR)-based detection scheme. However, the recent literature exhibits a shortage of works dealing with LUS image-based COVID-19 detection. In this paper, a spectral mask enhancement (SpecMEn) scheme is introduced along with a histogram equalization pre-processing stage to reduce the noise effect in LUS images prior to utilizing them for feature extraction. In order to detect the COVID-19 cases, we propose to utilize the SpecMEn pre-processed LUS images in the deep learning (DL) models (namely the SpecMEn-DL method), which offers a better representation of some characteristics features in LUS images and results in very satisfactory classification performance. The performance of the proposed SpecMEn-DL technique is appraised by implementing some state-of-the-art DL models and comparing the results with related studies. It is found that the use of the SpecMEn scheme in DL techniques offers an average increase in accuracy and [Formula: see text] score of [Formula: see text] and [Formula: see text] , respectively, at the video-level. Comprehensive analysis and visualization of the intermediate steps manifest a very satisfactory detection performance creating a flexible and safe alternative option for the clinicians to get assistance while obtaining the immediate evaluation of the patients. Springer International Publishing 2021-07-09 /pmc/articles/PMC8269407/ /pubmed/34257953 http://dx.doi.org/10.1007/s13755-021-00154-8 Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021 |
spellingShingle | Research Sadik, Farhan Dastider, Ankan Ghosh Fattah, Shaikh Anowarul SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos |
title | SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos |
title_full | SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos |
title_fullStr | SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos |
title_full_unstemmed | SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos |
title_short | SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos |
title_sort | specmen-dl: spectral mask enhancement with deep learning models to predict covid-19 from lung ultrasound videos |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269407/ https://www.ncbi.nlm.nih.gov/pubmed/34257953 http://dx.doi.org/10.1007/s13755-021-00154-8 |
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