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Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection

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Abstract

Lung cancer is considered one of the leading causes of death all across the world. Various radiology-related fields increasingly have used Computer-aided diagnosis (CAD) systems. It just has already become a part of clinical work for lung cancer detection. In this article, we proposed an Adaptive Boost-based Grid Search Optimized Random Forest (Ada-GridRF) classifier that best optimized the hyperparameters of the base random forest model to identify the malignant and non-malignant nodules from the trained CT images. Improved performance speed and reduced computational complexity were the advantages of the proposed method. The proposed methodology was compared with other hyperparameter optimization techniques and also with different conventional approaches. It even outperformed the popular state-of-the-art deep learning techniques such as transfer learning and convolutional neural network. The experimental results proved that the proposed method yielded the best performance metrics of 97.97% accuracy, 100% sensitivity, 96% specificity, 96.08% precision, 98% F1-score, 4% False positives rate, and 99.8% Area under the ROC curve (AUC). It took only 8 msec to train the model. Thus, the proposed Ada-GridRF model can aid radiologists in fast lung cancer detection.

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References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer Statistics, 2021. CA Cancer J Clin 71:7–33. https://doi.org/10.3322/caac.21654

    Article  PubMed  Google Scholar 

  2. Gu Y, Chi J, Liu J, Yang L, Zhang B, Yu D, Zhao Y, Lu X (2021) A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Comput Biol Med 137:104806. https://doi.org/10.1016/j.compbiomed.2021.104806

    Article  PubMed  Google Scholar 

  3. Monkam P, Qi S, Ma H, Gao W, Yao Y, Qian W (2019) Detection and classification of pulmonary nodules using convolutional neural networks: a survey. IEEE Access 7:78075–78091. https://doi.org/10.1109/ACCESS.2019.2920980

    Article  Google Scholar 

  4. Wang Z, Xin J, Sun P, Lin Z, Yao Y, Gao X (2018) Improved lung nodule diagnosis accuracy using lung CT images with uncertain class. Comput Methods Programs Biomed 162:197–209. https://doi.org/10.1016/j.cmpb.2018.05.028

    Article  PubMed  Google Scholar 

  5. Farahani FV, Ahmadi A, Zarandi MHF (2018) Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning. Math Comput Simul 149:48–68. https://doi.org/10.1016/j.matcom.2018.02.001

    Article  Google Scholar 

  6. Lima LL, Ferreira JR, Oliveira MC (2020) Toward classifying small lung nodules with hyperparameter optimization of convolutional neural networks. Comput Intell. https://doi.org/10.1111/coin.12350

    Article  Google Scholar 

  7. Deepa P, Suganthi M (2020) A fuzzy shape representation of a segmented vessel tree and kernel—induced random forest classifier for the efficient prediction of lung cancer. J Supercomput 76:5801–5824. https://doi.org/10.1007/s11227-019-03002-5

    Article  Google Scholar 

  8. Gonçalves L, Novo J, Cunha A, Campilho A (2018) Learning lung nodule malignancy likelihood from radiologist annotations or diagnosis data. J Med Biol Eng 38:424–442. https://doi.org/10.1007/s40846-017-0317-2

    Article  Google Scholar 

  9. Hashim FA, Houssein EH, Mabrouk MS, Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Futur Gener Comput Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015

    Article  Google Scholar 

  10. Silva GLF, Valente TLA, Silva AC, Paiva AC, Gattass M (2018) Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. Comput Methods Programs Biomed 162:109–118. https://doi.org/10.1016/j.cmpb.2018.05.006

    Article  PubMed  Google Scholar 

  11. Zhang G, Yang Z, Gong L, Jiang S, Wang L, Zhang H (2020) Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations. Radiol Med 125:374–383. https://doi.org/10.1007/s11547-019-01130-9

    Article  PubMed  Google Scholar 

  12. Xia K, Chi J, Gao Y, Jiang Y, Wu C (2021) Adaptive aggregated attention network for pulmonary nodule classification. Appl Sci 11:1–15. https://doi.org/10.3390/app11020610

    Article  CAS  Google Scholar 

  13. Nobrega RVM, Filho PP, Rodrigues MB, Silva SPP, Dourado Junior CMJM, Albuquerque VHC (2020) Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks. Neural Comput Appl 32:11065–11082. https://doi.org/10.1007/s00521-018-3895-1

    Article  Google Scholar 

  14. Shen W et al (2017) Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit 61:663–673. https://doi.org/10.1016/j.patcog.2016.05.029

    Article  Google Scholar 

  15. Alharbi A (2018) An automated computer system based on genetic algorithm and fuzzy systems for lung cancer diagnosis. Int J Nonlinear Sci Numer Simul 19:583–594. https://doi.org/10.1515/ijnsns-2017-0048

    Article  CAS  Google Scholar 

  16. Feng PH, Lin YT, Lo CM (2018) A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings. Med Phys 45:5509–5514. https://doi.org/10.1002/mp.13241

    Article  PubMed  Google Scholar 

  17. Shakeel PM, Tolba A, Makhadmeh Z, Jaber MM (2020) Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks. Neural Comput Appl 32:777–790. https://doi.org/10.1007/s00521-018-03972-2

    Article  Google Scholar 

  18. Wang H, Zhou Z, Li Y, Chen Z, Lu P, Wang W, Liu W (2017) Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images. EJNMMI Res 7:11. https://doi.org/10.1186/s13550-017-0260-9

    Article  PubMed  PubMed Central  Google Scholar 

  19. Armato SG et al (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38:915–931. https://doi.org/10.1118/1.3528204

    Article  PubMed  PubMed Central  Google Scholar 

  20. Nishio M, Nishizawa M, Sugiyama O, Kojima R, Yakami M, Kuroda T, Togashi K (2018) Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. PLoS ONE. https://doi.org/10.1371/journal.pone.0195875

    Article  PubMed  PubMed Central  Google Scholar 

  21. Gopalakrishnan RC, Kuppusamy V (2014) Ant colony optimization approaches to clustering of lung nodules from CT images. Comput Math Methods Med. https://doi.org/10.1155/2014/572494

    Article  PubMed  PubMed Central  Google Scholar 

  22. Mary M, Priya A, Jawhar SJ (2020) Advanced lung cancer classification approach adopting modified graph clustering and whale optimisation-based feature selection technique accompanied by a hybrid ensemble classifier. IET. https://doi.org/10.1049/iet-ipr.2019.0178

    Article  Google Scholar 

  23. El-askary NS, Salem MA (2019) Feature extraction and analysis for lung nodule classification using random forest. In: Proceedings of the 2019 8th international conference on software and information engineering. https://doi.org/10.1145/3328833.3328872

  24. Merz CJ, Murphy PM (2010) UCI machine learning repository. University of California, School of Information and Computer Sciences, Irvine. http://archive.ics.uci.edu/ml. Accessed 20 Sep 2021

Download references

Acknowlegements

The authors acknowledge Dr. Prabhu B J, Registrar, Department of Radiology, Silchar Medical College, Assam, India, 788014 for manually annotating the malignant nodules.

Funding

The authors acknowledge the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, for providing the infrastructural facility to carry out this project under the project grant number SRG/2020/000617.

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Authors and Affiliations

  1. Bio-Medical Imaging Laboratory (BIOMIL), Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    Ananya Bhattacharjee, R. Murugan & Tripti Goel

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India

    Badal Soni

Authors
  1. Ananya Bhattacharjee
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  2. R. Murugan
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  3. Badal Soni
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  4. Tripti Goel
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Correspondence to R. Murugan.

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This article does not contain any studies with human participants performed by any of the authors. This article does not contain any studies with animals performed by any of the authors. This article does not contain any studies with human participants or animals performed by any of the authors.

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Bhattacharjee, A., Murugan, R., Soni, B. et al. Ada-GridRF: A Fast and Automated Adaptive Boost Based Grid Search Optimized Random Forest Ensemble model for Lung Cancer Detection. Phys Eng Sci Med 45, 981–994 (2022). https://doi.org/10.1007/s13246-022-01150-2

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  • DOI: https://doi.org/10.1007/s13246-022-01150-2

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