Lung Cancer Detection using Ensemble Techniques


Authors : Piyush Choudhari; Yash Soniminde; Anubhav Sharma; Prisha Shah; Amish Faye; Nita J. Mahale

Volume/Issue : Volume 9 - 2024, Issue 4 - April


Google Scholar : https://tinyurl.com/33phz93x

Scribd : https://tinyurl.com/5ey4ca28

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR1516

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : This paper implements a system for enhancing the detection of lung cancer through an ensemble approach, which amalgamates the predictive outputs generated by three distinct convolutional neural networks (CNNs): ResNet50, EfficientNet, and InceptionNet. Leveraging the diverse architectural features and learning capabilities of these CNNs, the ensemble method aims to synergistically fuse their individual predictions to achieve heightened accuracy and robustness in identifying potential lung cancer manifestations.

Keywords : Lung Cancer Detection; CNN; Ensemble Techniques; Resnet50; VGG16; Inceptionnet.

References :

  1. Nageswaran S, Arunkumar G, Bisht AK, Mewada S, Kumar JNVRS, Jawarneh M, Asenso E. Lung Cancer Classification and Prediction Using Machine Learning and Image Processing. Biomed Res Int. 2022 Aug 22;2022:1755460. doi: 10.1155/2022/1755460. Retraction in: Biomed Res Int. 2024 Jan 9;2024:9851527. PMID: 36046454; PMCID: PMC9424001.
  2. B. S, P. R and A. B, "Lung Cancer Detection using Machine Learning," 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2022, pp. 539-543, doi: 10.1109/ICAAIC53929.2022.9793061.
  3. https://my.clevelandclinic.org/health/diseases/4375-lung-cancer
  4. https://www.cancer.org/cancer/types/lung-cancer/about/what-is.html
  5. https://www.cdc.gov/cancer/lung/basic_info/index.html

This paper implements a system for enhancing the detection of lung cancer through an ensemble approach, which amalgamates the predictive outputs generated by three distinct convolutional neural networks (CNNs): ResNet50, EfficientNet, and InceptionNet. Leveraging the diverse architectural features and learning capabilities of these CNNs, the ensemble method aims to synergistically fuse their individual predictions to achieve heightened accuracy and robustness in identifying potential lung cancer manifestations.

Keywords : Lung Cancer Detection; CNN; Ensemble Techniques; Resnet50; VGG16; Inceptionnet.

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