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A bi-directional deep learning architecture for lung nodule semantic segmentation
Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. I...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453728/ https://www.ncbi.nlm.nih.gov/pubmed/36097497 http://dx.doi.org/10.1007/s00371-022-02657-1 |
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author | Bhattacharyya, Debnath Thirupathi Rao, N. Joshua, Eali Stephen Neal Hu, Yu-Chen |
author_facet | Bhattacharyya, Debnath Thirupathi Rao, N. Joshua, Eali Stephen Neal Hu, Yu-Chen |
author_sort | Bhattacharyya, Debnath |
collection | PubMed |
description | Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts. |
format | Online Article Text |
id | pubmed-9453728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-94537282022-09-08 A bi-directional deep learning architecture for lung nodule semantic segmentation Bhattacharyya, Debnath Thirupathi Rao, N. Joshua, Eali Stephen Neal Hu, Yu-Chen Vis Comput Original Article Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts. Springer Berlin Heidelberg 2022-09-08 /pmc/articles/PMC9453728/ /pubmed/36097497 http://dx.doi.org/10.1007/s00371-022-02657-1 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor 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 | Original Article Bhattacharyya, Debnath Thirupathi Rao, N. Joshua, Eali Stephen Neal Hu, Yu-Chen A bi-directional deep learning architecture for lung nodule semantic segmentation |
title | A bi-directional deep learning architecture for lung nodule semantic segmentation |
title_full | A bi-directional deep learning architecture for lung nodule semantic segmentation |
title_fullStr | A bi-directional deep learning architecture for lung nodule semantic segmentation |
title_full_unstemmed | A bi-directional deep learning architecture for lung nodule semantic segmentation |
title_short | A bi-directional deep learning architecture for lung nodule semantic segmentation |
title_sort | bi-directional deep learning architecture for lung nodule semantic segmentation |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9453728/ https://www.ncbi.nlm.nih.gov/pubmed/36097497 http://dx.doi.org/10.1007/s00371-022-02657-1 |
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