research-article

Feature Extraction and Analysis for Lung Nodule Classification using Random Forest

Authors:

Nada S. El-Askary,

Mohammed A.-M. Salem,

Mohamed I. RoushdyAuthors Info & Claims

ICSIE '19: Proceedings of the 8th International Conference on Software and Information Engineering

Pages 248 - 252

https://doi.org/10.1145/3328833.3328872

Published: 09 April 2019 Publication History

Abstract

Early detection of lung nodule decreases the risk of advanced stages in lung cancer disease. Random forest (RF), a machine learning classifier, is used to detect the lung nodules and classify soft-tissues into nodules and non-nodules. A lung nodule classification approach is proposed to improve early detection for nodules. A five stages model has been built and tested using 165 cases from the LIDC database. Stage 1 is image acquisition and preprocessing. Stage 2 is extracting 119 features from the CT image. Stage 3 is refining feature vectors by removing all duplicate instances and undersampling the non-nodule class. Stage 4 is tuning the RF parameters. Stage 5 is examining different collections from the extracted feature sets to select those scores best for classification. The accuracy achieved by RF is the highest compared to other machine learning classifiers such as KNN, SVM, and DT. The proposed method aimed to analyze and select features that maximize classification results. Pixel based feature set and wavelet-based set scored best for higher accuracy. RF was tuned with 170 trees and 0.007 for in-bag fraction. Best results were achieved by the proposed model are 90.67%, 90.8% and 90.73% for sensitivity, specificity, and accuracy respectively.

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Cited By

A.R. BR.S. VS.S. K(2023)LCD-Capsule Network for the Detection and Classification of Lung Cancer on Computed Tomography ImagesMultimedia Tools and Applications10.1007/s11042-023-14893-182:24(37573-37592)Online publication date: 23-Mar-2023
https://doi.org/10.1007/s11042-023-14893-1
Nancy DKishan SRane KKaliyaperumal DMeenakshi DSuartama I(2022)Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer DetectionInternational Journal of Electrical and Electronics Research10.37391/ijeer.10042310:4(888-894)Online publication date: 30-Dec-2022
https://doi.org/10.37391/ijeer.100423
Donga HKarlapati JDesineedi HPeriasamy PTR S(2022)Effective Framework for Pulmonary Nodule Classification from CT Images Using the Modified Gradient Boosting MethodApplied Sciences10.3390/app1216826412:16(8264)Online publication date: 18-Aug-2022
https://doi.org/10.3390/app12168264
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Index Terms

Feature Extraction and Analysis for Lung Nodule Classification using Random Forest
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods
        Bagging

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ICSIE '19: Proceedings of the 8th International Conference on Software and Information Engineering

April 2019

276 pages

ISBN:9781450361057

DOI:10.1145/3328833

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 April 2019

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ICSIE '19: 2019 8th International Conference on Software and Information Engineering

April 9 - 12, 2019

Cairo, Egypt

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Cited By

A.R. BR.S. VS.S. K(2023)LCD-Capsule Network for the Detection and Classification of Lung Cancer on Computed Tomography ImagesMultimedia Tools and Applications10.1007/s11042-023-14893-182:24(37573-37592)Online publication date: 23-Mar-2023
https://doi.org/10.1007/s11042-023-14893-1
Nancy DKishan SRane KKaliyaperumal DMeenakshi DSuartama I(2022)Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer DetectionInternational Journal of Electrical and Electronics Research10.37391/ijeer.10042310:4(888-894)Online publication date: 30-Dec-2022
https://doi.org/10.37391/ijeer.100423
Donga HKarlapati JDesineedi HPeriasamy PTR S(2022)Effective Framework for Pulmonary Nodule Classification from CT Images Using the Modified Gradient Boosting MethodApplied Sciences10.3390/app1216826412:16(8264)Online publication date: 18-Aug-2022
https://doi.org/10.3390/app12168264
Singh AKumar RRastogi R(2022)Study of Machine Learning Models for the Prediction and Detection of Lungs Cancer2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)10.1109/SMART55829.2022.10047610(1243-1248)Online publication date: 16-Dec-2022
https://doi.org/10.1109/SMART55829.2022.10047610
Bhattacharjee AMurugan RSoni BGoel T(2022)Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer DetectionPhysical and Engineering Sciences in Medicine10.1007/s13246-022-01150-245:3(981-994)Online publication date: 30-Jun-2022
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Li YMansmann UDu SHornung R(2021)Synergistic Effects of Different Levels of Genomic Data for the Staging of Lung Adenocarcinoma: An Illustrative StudyGenes10.3390/genes1212187212:12(1872)Online publication date: 24-Nov-2021
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Kumar MKumar VSharma A(2021)Segmentation and Prediction of Lung Cancer CT Scans through Nodules using Ensemble Deep Learning Approach2021 IEEE Mysore Sub Section International Conference (MysuruCon)10.1109/MysuruCon52639.2021.9641698(781-785)Online publication date: 24-Oct-2021
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El-Askary NSalem MRoushdy M(2019)Lung Nodule Detection and Classification using Random Forest: A Review2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)10.1109/ICICIS46948.2019.9014706(105-111)Online publication date: Dec-2019
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