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Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework
Pneumonia is one of the hazardous diseases that lead to life insecurity. It needs to be diagnosed at the initial stages to prevent a person from more damage and help them save their lives. Various techniques are used to identify pneumonia, including chest X-ray, blood culture, sputum culture, fluid...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136378/ https://www.ncbi.nlm.nih.gov/pubmed/34035563 http://dx.doi.org/10.1007/s00521-021-06102-7 |
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author | Yi, Rong Tang, Lanying Tian, Yuqiu Liu, Jie Wu, Zhihui |
author_facet | Yi, Rong Tang, Lanying Tian, Yuqiu Liu, Jie Wu, Zhihui |
author_sort | Yi, Rong |
collection | PubMed |
description | Pneumonia is one of the hazardous diseases that lead to life insecurity. It needs to be diagnosed at the initial stages to prevent a person from more damage and help them save their lives. Various techniques are used to identify pneumonia, including chest X-ray, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Chest X-ray is the most widely used method to diagnose pneumonia and is considered one of the most reliable approaches. To analyse chest X-ray images accurately, an expert radiologist needs expertise and experience in the desired domain. However, human-assisted approaches have some drawbacks: expert availability, treatment cost, availability of diagnostic tools, etc. Hence, the need for an intelligent and automated system comes into place that operates on chest X-ray images and diagnoses pneumonia. The primary purpose of technology is to develop algorithms and tools that assist humans and make their lives easier. This study proposes a scalable and interpretable deep convolutional neural network (DCNN) to identify pneumonia using chest X-ray images. The proposed modified DCNN model first extracts useful features from the images and then classifies them into normal and pneumonia classes. The proposed system has been trained and tested on chest X-ray images dataset. Various performance metrics have been utilized to inspect the stability and efficacy of the proposed model. The experimental result shows that the proposed model's performance is greater compared to the other state-of-the-art methodologies used to identify pneumonia. |
format | Online Article Text |
id | pubmed-8136378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-81363782021-05-21 Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework Yi, Rong Tang, Lanying Tian, Yuqiu Liu, Jie Wu, Zhihui Neural Comput Appl S.i: ML4BD_SHS Pneumonia is one of the hazardous diseases that lead to life insecurity. It needs to be diagnosed at the initial stages to prevent a person from more damage and help them save their lives. Various techniques are used to identify pneumonia, including chest X-ray, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Chest X-ray is the most widely used method to diagnose pneumonia and is considered one of the most reliable approaches. To analyse chest X-ray images accurately, an expert radiologist needs expertise and experience in the desired domain. However, human-assisted approaches have some drawbacks: expert availability, treatment cost, availability of diagnostic tools, etc. Hence, the need for an intelligent and automated system comes into place that operates on chest X-ray images and diagnoses pneumonia. The primary purpose of technology is to develop algorithms and tools that assist humans and make their lives easier. This study proposes a scalable and interpretable deep convolutional neural network (DCNN) to identify pneumonia using chest X-ray images. The proposed modified DCNN model first extracts useful features from the images and then classifies them into normal and pneumonia classes. The proposed system has been trained and tested on chest X-ray images dataset. Various performance metrics have been utilized to inspect the stability and efficacy of the proposed model. The experimental result shows that the proposed model's performance is greater compared to the other state-of-the-art methodologies used to identify pneumonia. Springer London 2021-05-20 2023 /pmc/articles/PMC8136378/ /pubmed/34035563 http://dx.doi.org/10.1007/s00521-021-06102-7 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 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 | S.i: ML4BD_SHS Yi, Rong Tang, Lanying Tian, Yuqiu Liu, Jie Wu, Zhihui Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework |
title | Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework |
title_full | Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework |
title_fullStr | Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework |
title_full_unstemmed | Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework |
title_short | Identification and classification of pneumonia disease using a deep learning-based intelligent computational framework |
title_sort | identification and classification of pneumonia disease using a deep learning-based intelligent computational framework |
topic | S.i: ML4BD_SHS |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136378/ https://www.ncbi.nlm.nih.gov/pubmed/34035563 http://dx.doi.org/10.1007/s00521-021-06102-7 |
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