Skip to main content
SpringerLink
Log in

A Novel Assessment of Lung Cancer Classification System Using Binary Grasshopper with Artificial Bee Optimisation Algorithm with Double Deep Neural Network Classifier

  • ORIGINAL CONTRIBUTION
  • Published:
Journal of The Institution of Engineers (India): Series B Aims and scope Submit manuscript

Abstract

Lung cancer is the leading cause of death from cancer worldwide. Finding pulmonary nodules is a critical step in the diagnosis of early-stage lung cancer. It has the potential to become a tumour. Computed tomography (CT) scans for lung disease analysis offer useful data. Finding malignant pulmonary nodules and determining whether lung cancer is benign or malignant are the main goals. Before further image preparation, image denoising is an important process for removing noise from images (feature extraction, segmentation, surface detection, and so on) maximising the preservation of edges and other intact features. This study employs a novel evolutionary method dubbed the binary grasshopper optimisation algorithm in order to address some of the shortcomings of feature selection and provide an efficient feature selection algorithm, the artificial bee colony (BGOA-ABC) algorithm enhance categorisation. Then, to categorise the chosen features, we employ a hybrid classifier known as a double deep neural network (DDNN) algorithm. A technique used by MATLAB that segments impacted areas utilising improved IPCT (Profuse) aggregation technology employing datasets from the cancer image archive (CIA), the image database resource initiative (IDRI), and the lung imaging database consortium. Various performance metrics are evaluated and associated with different cutting-edge techniques, classifiers that are already in use.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Availability of Data and Materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

CT:

Computed tomography

BGOA-ABC:

Binary grasshopper optimisation algorithm artificial bee colony

CIA:

Cancer image archive

LIDC-IDRI:

Lung imaging database consortium, and the image database resource initiative

MRI:

Magnetic resonance imaging

M-CNN:

Multilayer convolutional neural network

CAD:

Computer-aided diagnostic

MRT:

Mean residence time

CMM:

Continuous Markov models

MLE:

Maximum likelihood estimation

RGB:

Red–green–blue

NLM:

Non-local mean

ANLM:

Adaptive nonlocal means

DDNN:

Double deep neural network

MSE:

Mean square error

PSNR:

Peak signal-to-noise ratio

References

  1. M. Palaniappan, M. Annamalai. Advances in signal and image processing in biomedical applications. (2019). https://doi.org/10.5772/intechopen.88759

  2. S. Kolli, V. Praveen, J. Ashok, A. Manikandan. Internet of things for pervasive and personalized healthcare: architecture, technologies, components, applications, and prototype development. (2023). https://doi.org/10.4018/978-1-6684-8913-0.ch008

  3. T. Guo, J. Dong, H. Li, Simple convolutional neural network on image classification. In Proceedings of the 2nd International Conference ICBDA, Beijing, China, 10–12 March 2017.

  4. A. Ivanov, A. Zhilenkov, The prospects of use of deep learning neural networks in problems of dynamicimages recognition. In Proceedings of the EIConRus, Moscow, Russia, 29 January–1 February 2018.

  5. H.P. Chan, L.M. Hadjiiski, R.K. Samala, Computer-aided diagnosis in the era of deep learning. Med. Phys. 47(5), e218–e227 (2020)

    Article  PubMed  Google Scholar 

  6. A.R. Venmathi, S. David, E. Govinda, K. Ganapriya, R. Dhanapal and A. Manikandan, An automatic brain tumors detection and classification using deep convolutional neural network with VGG-19. in 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 2023, pp 1–5 (2023). https://doi.org/10.1109/ICAECA56562.2023.10200949

  7. L. Ficsor, V.S. Varga, A. Tagscherer, Z. Tulassay, B. Molnar, Automated classification of inflammation in colon histological sections based on digital microscopy and advanced image analysis. Cytometry A 73A(3), 230–237 (2008)

    Article  Google Scholar 

  8. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)

    Article  ADS  PubMed  CAS  MATH  Google Scholar 

  9. M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, S. Mougiakakou, Lung pattern classification for interstitiallung diseases using a deep convolutional neuralnetwork. IEEE Trans. Med. Imaging 35(5), 1207–1216 (2016)

    Article  PubMed  Google Scholar 

  10. S. Kolli, M. Ranjani, P. Kavitha, D.A.P. Daniel and A. Chandramauli, Prediction of water quality parameters by IoT and machine learning. in 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2023, pp. 1–5, https://doi.org/10.1109/ICCCI56745.2023.10128475

  11. H. Yu, Z. Zhou, Q. Wang, Deep learning assisted predict of lung cancer on computed tomography images using the adaptive hierarchical heuristic mathematical model (2020) [online]. Available: https://doi.org/10.1109/ACCESS.2020.2992645

  12. K. Bommaraju, A. Manikandan, S. Ramalingam. Aided system for visually impaired people in bus transport using intel galileo Gen-2: Technical note. International Journal of Vehicle Structures and Systems, 9(2), 110–112 (2017). https://doi.org/10.4273/ijvss.9.2.09

    Article  Google Scholar 

  13. S. Kolli, M. Hasan, B. Hazela, A. A. J. Pazhani, Secure the smart grid by machine learning. in International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2023, pp. 1–4, https://doi.org/10.1109/ICCCI56745.2023.10128269

  14. K. Srinivas, J. Aswini, P. Patro, D. Kumar, Functional overview of integration of AIML with 5G and beyond the network, in International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2023, pp. 1–5, https://doi.org/10.1109/ICCCI56745.2023.10128466

  15. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.C. Chen, Mobilenetv2: inverted residuals and linear bottlenecks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520

  16. S.G. Armato III., G. McLennan, L. Bidaut, M.F. McNitt-Gray, C.R. Meyer, A.P. Reeves et al., 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(2), 915–931 (2011)

    Article  PubMed  PubMed Central  Google Scholar 

  17. A. Asuntha, A. Srinivasan. Deep learning for lung Cancer detection and classification. Multimedia Tools and Applications 1–32 (2020)

  18. X.Z. Zhao, L.Y. Liu, S.L. Qi, Y.Y. Teng, J.H. Li, W. Qian. Agile convolutional neural network for pulmonary nodule classification using CT images. International Journal of Computer Assisted Radiology and Surgery 13, 585–595 (2018)

    MATH  Google Scholar 

  19. X.L. Liu, F. Hou, A. Hao. Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recognition 77, 262–275 (2018)

    Article  ADS  MATH  Google Scholar 

  20. D. Kumar, A. Wong, D.A. Clausi. Lung nodule classification using deep features in CT images. in Proceedings of the 2015 12th Conference on Computer and Robot Vision (CRV), Halifax, NS, Canada, 3–5 June 2015, pp. 133–138

  21. W. Shen, M. Zhou, F. Yang, C.Y. Yang, J. Tian. Multi-scale convolutional neural networks for lung nodule classification. in information processing in medical imaging (Springer, Cham, Switzerland, 2015) pp 588–599

  22. Y. Xie, J. Zhang, Y. Xia, Semi-supervised adversarial model for benign–malignant lung nodule classification on chest CT. Medical Image Analysis 57, 237–248 (2019)

    Article  PubMed  Google Scholar 

  23. S.R. Sannasi Chakravarthy, H. Rajaguru. Lung cancer detection using probabilistic neural network with modified crow-search algorithm. Asian Pacific Journal of Cancer Prevention 20(7), 2159 (2019)

    Article  MATH  Google Scholar 

  24. S. Kolli, A.P. Krishna, M. Sreedevi. A meta heuristic multi-view data analysis over unconditional labeled material: An intelligence OCMHAMCV: Multi-view data analysis. Scalable Computing: Practice and Experience 23(4), 275–289 (2022)

    MATH  Google Scholar 

  25. S. Kolli, M. Sreedevi. Adaptive clustering approach to handle multi similarity index for uncertain categorical data streams. Journal of Advanced Research in Dynamical and Control Systems 10(04) (2018)

  26. R. Ali, A. Manikandan, J. Xu. A novel framework of adaptive fuzzy-GLCM segmentation and fuzzy with capsules network (F-CapsNet) classification. Neural Computing and Applications (2023). https://doi.org/10.1007/s00521-023-08666-y

    Article  PubMed  MATH  Google Scholar 

  27. M. Annamalai, P. Muthiah. An early prediction of tumor in heart by cardiac masses classification in echocardiogram images using robust back propagation neural network classifier. Brazilian Archives of Biology and Technology 65. (2022). https://doi.org/10.1590/1678-4324-2022210316

  28. A. Manikandan, M. Ponni Bala, Intracardiac mass detection and classification using double convolutional neural network classifier. Journal of Engineering Research 11(2A), 272–280. (2023). https://doi.org/10.36909/jer.12237

    Article  Google Scholar 

  29. D. Balamurugan, S.S. Aravinth, P. Reddy, A. Rupani, A. Manikandan. Multiview objects recognition using deep learning-based wrap-CNN with voting scheme. Neural Processing Letters 54, 1–27 (2022). https://doi.org/10.1007/s11063-021-10679-4

    Article  Google Scholar 

  30. A. Manikandan. A survey on classification of medical images using deep learning. Journal of Image Processing and Intelligent Remote Sensing (JIPIRS) 1(01), 5–14. (2021). https://doi.org/10.55529/jipirs.11.5.14

    Article  MATH  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

No funding was received by any government or private concern. “No funding was obtained for this study”.

Author information

Authors and Affiliations

  1. Department of Information Technology, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, 500090, India

    Srinivas Kolli & Balakesava Reddy Parvathala

Authors
  1. Srinivas Kolli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Balakesava Reddy Parvathala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srinivas Kolli.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kolli, S., Parvathala, B.R. A Novel Assessment of Lung Cancer Classification System Using Binary Grasshopper with Artificial Bee Optimisation Algorithm with Double Deep Neural Network Classifier. J. Inst. Eng. India Ser. B (2024). https://doi.org/10.1007/s40031-024-01027-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40031-024-01027-w

Keywords

Search

Navigation