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An Effective Approach for Automated Lung Node Detection using CT Scans

BACKGROUND: Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures....

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Autores principales: Moragheb, Mohammad Amin, Badie, Ali, Noshad, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Shiraz University of Medical Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395629/
https://www.ncbi.nlm.nih.gov/pubmed/36059280
http://dx.doi.org/10.31661/jbpe.v0i0.2110-1412
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author Moragheb, Mohammad Amin
Badie, Ali
Noshad, Ali
author_facet Moragheb, Mohammad Amin
Badie, Ali
Noshad, Ali
author_sort Moragheb, Mohammad Amin
collection PubMed
description BACKGROUND: Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures. OBJECTIVE: This study aimed to introduce an improved hybrid approach for efficient nodule mask generation and false-positive reduction. MATERIAL AND METHODS: In this experimental study, nodule segmentation preprocessing was conducted to prepare the input computed tomography (CT) scans for the U-Net convolutional neural network (CNN) model, and includes the normalization of CT scans and transfer of pixel values corresponding to the radiodensity of Hounsfield Units (HU). A U-Net CNN was developed based on lung CT scans for nodule identification. RESULTS: The U-net model converged to a dice coefficient of 0.678 with a sensitivity of 75%. Many false positives were considered in every real positive, at 11.1, reduced in the proposed CNN to 2.32 FPs (False Positive) per TP (True Positive). CONCLUSION: Based on the disadvantages of the largest nodule, the similarity of extracted features of the current study with those of others was imperative. The improved hybrid approach introduced was useful for other image classification tasks as expected.
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spelling pubmed-93956292022-09-02 An Effective Approach for Automated Lung Node Detection using CT Scans Moragheb, Mohammad Amin Badie, Ali Noshad, Ali J Biomed Phys Eng Original Article BACKGROUND: Pulmonary or benign nodules are classified as nodules with a diameter of 3 cm or less and defined as non-cancerous nodules. The early diagnosis of malignant lung nodules is important for a more reliable prognosis of lung cancer and less invasive chemotherapy and radiotherapy procedures. OBJECTIVE: This study aimed to introduce an improved hybrid approach for efficient nodule mask generation and false-positive reduction. MATERIAL AND METHODS: In this experimental study, nodule segmentation preprocessing was conducted to prepare the input computed tomography (CT) scans for the U-Net convolutional neural network (CNN) model, and includes the normalization of CT scans and transfer of pixel values corresponding to the radiodensity of Hounsfield Units (HU). A U-Net CNN was developed based on lung CT scans for nodule identification. RESULTS: The U-net model converged to a dice coefficient of 0.678 with a sensitivity of 75%. Many false positives were considered in every real positive, at 11.1, reduced in the proposed CNN to 2.32 FPs (False Positive) per TP (True Positive). CONCLUSION: Based on the disadvantages of the largest nodule, the similarity of extracted features of the current study with those of others was imperative. The improved hybrid approach introduced was useful for other image classification tasks as expected. Shiraz University of Medical Sciences 2022-08-01 /pmc/articles/PMC9395629/ /pubmed/36059280 http://dx.doi.org/10.31661/jbpe.v0i0.2110-1412 Text en Copyright: © Journal of Biomedical Physics and Engineering https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 Unported License, ( http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Moragheb, Mohammad Amin
Badie, Ali
Noshad, Ali
An Effective Approach for Automated Lung Node Detection using CT Scans
title An Effective Approach for Automated Lung Node Detection using CT Scans
title_full An Effective Approach for Automated Lung Node Detection using CT Scans
title_fullStr An Effective Approach for Automated Lung Node Detection using CT Scans
title_full_unstemmed An Effective Approach for Automated Lung Node Detection using CT Scans
title_short An Effective Approach for Automated Lung Node Detection using CT Scans
title_sort effective approach for automated lung node detection using ct scans
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395629/
https://www.ncbi.nlm.nih.gov/pubmed/36059280
http://dx.doi.org/10.31661/jbpe.v0i0.2110-1412
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