Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis

We consider the automatic hardness determination of a chest X-ray image and the effect of pre-filtering of the training and validation samples on the performance of the classification algorithm of tuberculosis diagnosis from chest X-rays. Convolutional neural networks are used in automatic hardness...

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Autores principales: Pchelintsev, Ya. A., Khvostikov, A. V., Krylov, A. S., Parolina, L. E., Nikoforova, N. A., Shepeleva, L. P., Prokop’ev, E. S., Farias, M., Yong, Ding
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838494/
http://dx.doi.org/10.1007/s10598-023-09568-3
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author Pchelintsev, Ya. A.
Khvostikov, A. V.
Krylov, A. S.
Parolina, L. E.
Nikoforova, N. A.
Shepeleva, L. P.
Prokop’ev, E. S.
Farias, M.
Yong, Ding
author_facet Pchelintsev, Ya. A.
Khvostikov, A. V.
Krylov, A. S.
Parolina, L. E.
Nikoforova, N. A.
Shepeleva, L. P.
Prokop’ev, E. S.
Farias, M.
Yong, Ding
author_sort Pchelintsev, Ya. A.
collection PubMed
description We consider the automatic hardness determination of a chest X-ray image and the effect of pre-filtering of the training and validation samples on the performance of the classification algorithm of tuberculosis diagnosis from chest X-rays. Convolutional neural networks are used in automatic hardness determination and tuberculosis diagnosis. The results of the present study are compared with those from different datasets, including datasets pruned by image hardness criteria.
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spelling pubmed-98384942023-01-17 Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis Pchelintsev, Ya. A. Khvostikov, A. V. Krylov, A. S. Parolina, L. E. Nikoforova, N. A. Shepeleva, L. P. Prokop’ev, E. S. Farias, M. Yong, Ding Comput Math Model Article We consider the automatic hardness determination of a chest X-ray image and the effect of pre-filtering of the training and validation samples on the performance of the classification algorithm of tuberculosis diagnosis from chest X-rays. Convolutional neural networks are used in automatic hardness determination and tuberculosis diagnosis. The results of the present study are compared with those from different datasets, including datasets pruned by image hardness criteria. Springer US 2023-01-12 2022 /pmc/articles/PMC9838494/ http://dx.doi.org/10.1007/s10598-023-09568-3 Text en © Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Pchelintsev, Ya. A.
Khvostikov, A. V.
Krylov, A. S.
Parolina, L. E.
Nikoforova, N. A.
Shepeleva, L. P.
Prokop’ev, E. S.
Farias, M.
Yong, Ding
Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis
title Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis
title_full Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis
title_fullStr Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis
title_full_unstemmed Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis
title_short Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis
title_sort hardness analysis of x-ray images for neural-network tuberculosis diagnosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838494/
http://dx.doi.org/10.1007/s10598-023-09568-3
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