Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Javed, Muhammad Ashar, Bin Liaqat, Hannan, Meraj, Talha, Alotaibi, Aziz, Alshammari, Majid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
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
_version_ 1785083813263572992
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
work_keys_str_mv AT javedmuhammadashar identificationandclassificationoflungsfocalopacityusingcnnsegmentationandoptimalfeatureselection
AT binliaqathannan identificationandclassificationoflungsfocalopacityusingcnnsegmentationandoptimalfeatureselection
AT merajtalha identificationandclassificationoflungsfocalopacityusingcnnsegmentationandoptimalfeatureselection
AT alotaibiaziz identificationandclassificationoflungsfocalopacityusingcnnsegmentationandoptimalfeatureselection
AT alshammarimajid identificationandclassificationoflungsfocalopacityusingcnnsegmentationandoptimalfeatureselection