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Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model

According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the big...

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Autores principales: Srivastava, Durgesh, Srivastava, Santosh Kumar, Khan, Surbhi Bhatia, Singh, Hare Ram, Maakar, Sunil K., Agarwal, Ambuj Kumar, Malibari, Areej A., Albalawi, Eid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669960/
https://www.ncbi.nlm.nih.gov/pubmed/37998620
http://dx.doi.org/10.3390/diagnostics13223485
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author Srivastava, Durgesh
Srivastava, Santosh Kumar
Khan, Surbhi Bhatia
Singh, Hare Ram
Maakar, Sunil K.
Agarwal, Ambuj Kumar
Malibari, Areej A.
Albalawi, Eid
author_facet Srivastava, Durgesh
Srivastava, Santosh Kumar
Khan, Surbhi Bhatia
Singh, Hare Ram
Maakar, Sunil K.
Agarwal, Ambuj Kumar
Malibari, Areej A.
Albalawi, Eid
author_sort Srivastava, Durgesh
collection PubMed
description According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model’s training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods.
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spelling pubmed-106699602023-11-20 Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model Srivastava, Durgesh Srivastava, Santosh Kumar Khan, Surbhi Bhatia Singh, Hare Ram Maakar, Sunil K. Agarwal, Ambuj Kumar Malibari, Areej A. Albalawi, Eid Diagnostics (Basel) Article According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model’s training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods. MDPI 2023-11-20 /pmc/articles/PMC10669960/ /pubmed/37998620 http://dx.doi.org/10.3390/diagnostics13223485 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Srivastava, Durgesh
Srivastava, Santosh Kumar
Khan, Surbhi Bhatia
Singh, Hare Ram
Maakar, Sunil K.
Agarwal, Ambuj Kumar
Malibari, Areej A.
Albalawi, Eid
Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model
title Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model
title_full Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model
title_fullStr Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model
title_full_unstemmed Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model
title_short Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model
title_sort early detection of lung nodules using a revolutionized deep learning model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669960/
https://www.ncbi.nlm.nih.gov/pubmed/37998620
http://dx.doi.org/10.3390/diagnostics13223485
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