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Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection
Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient's survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10396675/ https://www.ncbi.nlm.nih.gov/pubmed/37538561 http://dx.doi.org/10.1155/2023/6357252 |
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author | Javed, Muhammad Ashar Bin Liaqat, Hannan Meraj, Talha Alotaibi, Aziz Alshammari, Majid |
author_facet | Javed, Muhammad Ashar Bin Liaqat, Hannan Meraj, Talha Alotaibi, Aziz Alshammari, Majid |
author_sort | Javed, Muhammad Ashar |
collection | PubMed |
description | Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient's survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%. |
format | Online Article Text |
id | pubmed-10396675 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-103966752023-08-03 Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection Javed, Muhammad Ashar Bin Liaqat, Hannan Meraj, Talha Alotaibi, Aziz Alshammari, Majid Comput Intell Neurosci Research Article Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient's survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%. Hindawi 2023-07-26 /pmc/articles/PMC10396675/ /pubmed/37538561 http://dx.doi.org/10.1155/2023/6357252 Text en Copyright © 2023 Muhammad Ashar Javed et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Javed, Muhammad Ashar Bin Liaqat, Hannan Meraj, Talha Alotaibi, Aziz Alshammari, Majid Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection |
title | Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection |
title_full | Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection |
title_fullStr | Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection |
title_full_unstemmed | Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection |
title_short | Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection |
title_sort | identification and classification of lungs focal opacity using cnn segmentation and optimal feature selection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10396675/ https://www.ncbi.nlm.nih.gov/pubmed/37538561 http://dx.doi.org/10.1155/2023/6357252 |
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