Skip to main content
Springer Nature Link
Log in

RETRACTED ARTICLE: Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks

  • Intelligent Biomedical Data Analysis and Processing
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

This article was retracted on 29 April 2024

This article has been updated

Abstract

Today, most of the people are affected by lung cancer, mainly because of the genetic changes of the tissues in the lungs. Other factors such as smoking, alcohol, and exposure to dangerous gases can also be considered the contributory causes of lung cancer. Due to the serious consequences of lung cancer, the medical associations have been striving to diagnose cancer in its early stage of growth by applying the computer-aided diagnosis process. Although the CAD system at healthcare centers is able to diagnose lung cancer during its early stage of growth, the accuracy of cancer detection is difficult to achieve, mainly because of the overfitting of lung cancer features and the dimensionality of the feature set. Thus, this paper introduces the effective and optimized neural computing and soft computing techniques to minimize the difficulties and issues in the feature set. Initially, lung biomedical data were collected from the ELVIRA Biomedical Data Set Repository. The noise present in the data was eliminated by applying the bin smoothing normalization process. The minimum repetition and Wolf heuristic features were subsequently selected to minimize the dimensionality and complexity of the features. The selected lung features were analyzed using discrete AdaBoost optimized ensemble learning generalized neural networks, which successfully analyzed the biomedical lung data and classified the normal and abnormal features with great effectiveness. The efficiency of the system was then evaluated using MATLAB experimental setup in terms of error rate, precision, recall, G-mean, F-measure, and prediction rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

Change history

References

  1. World Cancer Report (2014) World Health Organization, Chapter 5.1. ISBN 92-832-0429-8

  2. https://www.naaccr.org/

  3. Lung Cancer—Patient Version. NCI. Archived from the original on 9 March 2016. Retrieved 5 Mar 2016

  4. Horn L, Lovly CM, Johnson DH (2015) Chapter 107: neoplasms of the lung. In: Kasper DL, Hauser SL, Jameson JL, Fauci AS, Longo DL, Loscalzo J (eds) Harrison’s principles of internal medicine, 19th edn. McGraw-Hill, New York. ISBN 978-0-07-180216-1

  5. Alberg AJ, Brock MV, Samet JM (2016) Chapter 52: epidemiology of lung cancer. In: Murray & Nadel’s textbook of respiratory medicine, 6th edn. Saunders Elsevier, Amsterdam. ISBN 978-1-4557-3383-5

  6. Collins LG, Haines C, Perkel R, Enck RE (2007) Lung cancer: diagnosis and management. Am Fam Phys 75(1):56–63. PMID 17225705. Archived from the original on 29 September 2007

  7. Rance B, Canuel V, Countouris H, Laurent-Puig P, Burgun A (2016) Integrating heterogeneous biomedical data for cancer research: the CARPEM infrastructure. Appl Clin Inform 7(2):260–274. https://doi.org/10.4338/aci-2015-09-ra-0125

    Article  Google Scholar 

  8. Lee SLA, Kouzani AZ, Hu EJ (2010) Random forest based lung nodule classification aided by clustering. Comput Med Imaging Graph 34:535–542

    Article  Google Scholar 

  9. Jinsa K, Gunavathi K (2014) Lung cancer classification using neural networks for CT images. Comput Methods Programs Biomed 113:202–209

    Article  Google Scholar 

  10. Diaz JM, Pinon RC, Solano G (2014) Lung cancer classification using genetic algorithm to optimize prediction models, IISA 2014. In: The 5th international conference on information, intelligence, systems and applications in IEEE

  11. Fang L, Zhao H, Wang P, Yu M, Yan J, Cheng W, Chen P (2015) Feature selection method based on mutual information and class separability for dimension reduction in multidimensional time series for clinical data. Biomed Signal Process Control 21:82–89

    Article  Google Scholar 

  12. Seelan LJ, Padma Suresh L, Krishna Veni SH (2016) Automatic extraction of Lung lesion by using optimized toboggan based approach with feature normalization and transfer learning methods. In: International conference on emerging technological trends (ICETT) in IEEE

  13. Kohad R, Ahire V (2015) Application of machine learning techniques for the diagnosis of lung cancer with ANT colony optimization. Int J Comput Appl. https://doi.org/10.5120/19928-2069

    Article  Google Scholar 

  14. Rebouças Filho PP, da Silva Barros AC, Ramalho GLB, Pereira CR, Papa JP (2017) Automated recognition of lung diseases in CT images based on the optimum-path forest classifier. Neural Comput Appl. https://doi.org/10.1007/s00521-017-3048-y

    Article  Google Scholar 

  15. de Rebouças ES, Marques RCP, Braga AM, Oliveira SAF (2018) New level set approach based on Parzen estimation for stroke segmentation in skull CT images. Soft Comput. https://doi.org/10.1007/s00500-018-3491-4

    Article  Google Scholar 

  16. Rebouças Filho PP, Cortez PC, da Silva Barros AC, Albuquerque VHC, Tavares JMRS (2017) Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Med Image Anal 35:503–516. https://doi.org/10.1016/j.media.2016.09.002

    Article  Google Scholar 

  17. Emarya E, Zawbaabc HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

  18. Sun T, Wanga J, Li X, Lv P, Liu F, Luo Y, Gao Q, Zhu H, Guo X (2013) Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set. Comput Methods Progr Biomed 111(2):519–524

    Article  Google Scholar 

  19. Bhattacharjee A et al (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. PNAS 98(24):13790–13795

    Article  Google Scholar 

  20. Pandey KK, Pradhan N (2014) An analytical and comparative study of various data preprocessing method in data mining. Int J Emerg Technol Adv Eng 4(10):174–180

    Google Scholar 

  21. Dodge Y (2003) The Oxford dictionary of statistical terms. Oxford University Press, Oxford. ISBN 0-19-920613-9 (entry for normalization of scores)

  22. Claypo N, Jaiyen S (2014) Opinion mining for Thai restaurant reviews using neural networks and mRMR feature selection. In: Computer science and engineering conference (ICSEC) 2014 international, pp 394–397

  23. Nguyen H, Franke K, Petrovic S (2010) Towards a generic feature-selection measure for intrusion detection. In: Proceeding of the international conference on pattern recognition (ICPR), Istanbul

  24. Einicke GA (2018) Maximum-entropy rate selection of features for classifying changes in knee and ankle dynamics during running. IEEE J Biomed Health Inform 28(4):1097–1103

    Article  Google Scholar 

  25. Peng HC, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238. https://doi.org/10.1109/TPAMI.2005.159(PMID 16119262. Program)

    Article  Google Scholar 

  26. Korayem L, Khorsid M, Kassem SS (2015) Using grey Wolf algorithm to solve the capacitated vehicle routing problem. In: Proceedings of the 3rd international conference on manufacturing, optimization, industrial and material engineering (MOIME’15). Institute of Physics Publishing, Bali

  27. Mohamed A-AA, El-Gaafary AAM, Mohamed YS, Hemeida AM (2015) Design static VAR compensator controller using artificial neural network optimized by modify Grey Wolf optimization. In: Proceedings of the international joint conference on neural networks (IJCNN’15), Anchorage

  28. Kégl B (2013) The return of AdaBoost.MH: multi-class Hamming trees. arXiv:1312.6086

  29. Rojas R (2009) AdaBoost and the super bowl of classifiers a tutorial introduction to adaptive boosting. Technical Report, Freie University, Berlin

  30. Xia J, Yokoya N, Iwasaki Y (2017) A novel ensemble classifier of hyperspectral and LiDAR data using morphological features. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 6185–6189. https://doi.org/10.1109/icassp.2017.7953345

  31. Mgbe CO, Mom JM, Igwue GA (2015) Performance evaluation of generalized regression neural network path loss prediction model in macrocellular environment. Perform Eval 2(2):204–208

    Google Scholar 

  32. Shakeel PM, Baskar S, Dhulipala VRS, Mishra S, Jaber MM (2018) Maintaining security and privacy in health care system using learning based deep-Q-networks. J Med Syst 42:186

    Article  Google Scholar 

  33. http://www-genome.wi.mit.edu/mpr/lung/

  34. Gordon GJ et al (2002) Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res 62:4963–4967

    Google Scholar 

  35. Beer DG et al (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 8(8):816–823

    Article  Google Scholar 

  36. Wigle DA et al (2002) Molecular profiling of non-small cell lung cancer and correlation with disease-free survival. Cancer Res 62:3005–3008

    Google Scholar 

  37. Luque-Baena RM, Urda D, Subirats JL, Franco L, Jerez JM (2014) Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data. Theor Biol Med Model 11(Suppl 1):S7. https://doi.org/10.1186/1742-4682-11-s1-s7

    Article  Google Scholar 

  38. Ayshwarya SS (2018) Lung cancer prediction using feed forward back propagation neural networks with optimal features. Int J Appl Eng Res 13(1):318–325. ISSN 0973-4562

  39. Zhao Z, Feng J, Jing K, Shi E (2017) A hybrid ACOR algorithm for pattern classification neural network training. In: International conference on computing intelligence and information system (CIIS) in IEEE

  40. Geng Y, Zhang L, Sun Y, Zhang Y, Yang N, Wu J (2016) Research on ant colony algorithm optimization neural network weights blind equalization algorithm. Int J Secur Appl 10(2):95–104. https://doi.org/10.14257/ijsia.2016.10.2.09

    Article  Google Scholar 

  41. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks (PDF). Adv Neural Inf Process Syst 1:1097–1105

    Google Scholar 

  42. Nunes TM, Coelho AL, Lima CA, Papa JP, de Albuquerque VHC (2014) EEG signal classification for epilepsy diagnosis via optimum path forest—a systematic assessment. Neurocomputing 136:103–123

    Article  Google Scholar 

  43. Rebouças Filho PP, Cortez PC, da Silva Barros AC, De Albuquerque VHC (2014) Novel adaptive balloon active contour method based on internal force for image segmentation—a systematic evaluation on synthetic and real images. Expert Syst Appl 41(17):7707–7721

    Article  Google Scholar 

  44. De Albuquerque VHC, Nunes TM, Pereira DR et al (2018) Robust automated cardiac arrhythmia detection in ECG beat signals. Neural Comput Appl 29:679. https://doi.org/10.1007/s00521-016-2472-8

    Article  Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research Group No. (RG-1438-027).

Author information

Authors and Affiliations

  1. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    P. Mohamed Shakeel

  2. Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia

    Amr Tolba & Zafer Al-Makhadmeh

  3. Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Kom, Egypt

    Amr Tolba

  4. Department of Computer Science, Dijlah University College, Baghdad, Iraq

    Mustafa Musa Jaber

Authors
  1. P. Mohamed Shakeel
    View author publications

    Search author on:PubMed Google Scholar

  2. Amr Tolba
    View author publications

    Search author on:PubMed Google Scholar

  3. Zafer Al-Makhadmeh
    View author publications

    Search author on:PubMed Google Scholar

  4. Mustafa Musa Jaber
    View author publications

    Search author on:PubMed Google Scholar

Corresponding authors

Correspondence to P. Mohamed Shakeel or Amr Tolba.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-024-09875-9

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shakeel, P.M., Tolba, A., Al-Makhadmeh, Z. et al. RETRACTED ARTICLE: Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks. Neural Comput & Applic 32, 777–790 (2020). https://doi.org/10.1007/s00521-018-03972-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-03972-2

Keywords