[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

Automated breast cancer detection using hybrid extreme learning machine classifier

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Breast cancer has been identified as one of the major diseases that have led to the death of women in recent decades. Mammograms are extensively used by physicians to diagnose breast cancer. The selection of appropriate image enhancement, segmentation, feature extraction, feature selection and prediction algorithm plays an essential role in precise cancer diagnosis on mammograms and remains as a major task in the research field. Classification methods predict the class label for unlabeled dataset based on its proximity to the learnt pattern. The selected features obtained after feature selection are classified using an extreme learning machines (ELM) to three classes with the classes being normal, benign and malignant. Low generalisation performance is the problem which happens due to the ill-conditioned output matrix of the hidden layer of the classifier. The optimisation algorithms would resolve these issues because of their global searching ability. This paper proposes ELM with Fruitfly Optimisation Algorithm (ELM-FOA) to tune the input weight to obtain optimum output at the ELM’s hidden node to obtain the solution analytically. The testing sensitivity and precision of ELM-FOA are 97.5% and 100% respectively. The developed method can detect the calcifications and tumours with 99.04% accuracy. The optimal selection of preprocessing and segmentation algorithms, features from multiple feature filters and the efficient classifier algorithm meliorate the performance of the approach.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Ahmed L, Iqbal MM, Aldabbas H et al (2020) Images data practices for semantic segmentation of breast cancer using deep neural network. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01680-1

    Article  Google Scholar 

  • Akselrod-Ballin A, Karlinsky L, Alpert S, Hashoul S, Ben-Ari R, Barkan E (2019) A CNN based method for automatic mass detection and classification in mammograms. Comput Methods Biomech Biomed Eng Imaging Vis 7:242–249

    Article  Google Scholar 

  • Al-masni MA, Al-antari MA, Park JM et al (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed 157:85–94

    Article  Google Scholar 

  • Beura S, Majhi B, Dash R (2015) Mammogram classification using two dimensional discrete wavelet transform and gray level co-occurrence matrix for detection of breast cancer. Neurocomputing 154:1–14

    Article  Google Scholar 

  • Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: a cancer. J Clin 68:394–424

    Google Scholar 

  • Dheeba J, Albert Singh N, Tamil Selvi S (2014) Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J Biomed Inform 49:45–52

    Article  Google Scholar 

  • Dhungel N, Carneiro G, Bradley AP (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 37:114–128

    Article  Google Scholar 

  • Eltoukhy MM, Elhoseny M, Hosny KM et al (2018) Computer aided detection of mammographic mass using exact Gaussian–Hermite moments. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0905-1

    Article  Google Scholar 

  • Fan M, Li Y, Zheng S, Peng W, Tang W, Li L (2019) Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network. Methods 166:103–111

    Article  Google Scholar 

  • Hayat M, Mabrouk MS, Sharawy A (2014) Computer aided detection system for micro-calcifications in digital mammograms. Comput Methods Programs Biomed 116:226–235

    Article  Google Scholar 

  • Jafar AM (2017) Deep Learning based computer aided diagnosis system for breast mammograms. Int J Adv Comput Sci Appl (IJACSA) 8(7):286–290

    Google Scholar 

  • Jiao ZhiCheng, Gao X, Wang Y, Li J (2018) A parasitic metric learning net for breast mass classification based on mammography. Pattern Recogn 75:292–301

    Article  Google Scholar 

  • John S, Melekoodappattu JG (2019) Extreme learning machine based classification for detecting micro-calcification in mammogram using multi scale features. IEEE Int Conf Comput Commun Inform. https://doi.org/10.1109/iccci.2019.8821877

    Article  Google Scholar 

  • Kelder A, Lederman D, Zheng B, Zigel Y (2018) A new computer- aided detection approach based on analysis of local and global mammographic feature asymmetry. Med Phys 45:1459–1470

    Article  Google Scholar 

  • Kshema M, Melekoodappattu JG (2017a) Efficient mammographic mass segmentation techniques: a review. IEEE Int Conf Wirel Commun Signal Process Netw. https://doi.org/10.1109/wispnet.2017.8300160

    Article  Google Scholar 

  • Kshema M, Melekoodappattu JG (2017b) Preprocessing filters for mammogram images: a review. IEEE Int Conf Emerg Devices Smart Syst. https://doi.org/10.1109/icedss.2017.8073694

    Article  Google Scholar 

  • Lan Y, Ren H, Wan J (2012) A hybrid classifier for mammography. In: Fourth international conference on computational and information sciences, pp 309–312

  • Llado X, Oliver A, Freixenet J, Marti R, Marti J (2009) A textural approach for mass false positive reduction in mammography. Comput Med Imaging Graph 33:415–422

    Article  Google Scholar 

  • Melekoodappattu JG, Subbian PS (2017) Efficient preprocessing filters and mass segmentation techniques for mammogram images. IEEE Int Conf Circuits Syst. https://doi.org/10.1109/iccs1.2017.8326032

    Article  Google Scholar 

  • Melekoodappattu JG, Subbian P (2019) A hybridized ELM for automatic micro calcification detection in mammogram images based on multi-scale features. J Med Syst 43:183. https://doi.org/10.1007/s10916-019-1316-3

    Article  Google Scholar 

  • Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and adaboost with random forests. Neurocomputing 177:188–197

    Article  Google Scholar 

  • Nguyen V, Nguyen D, Nguyen H, Bui D, Nguyen T (2012) Automatic identification of massive lesions in digitalized mammograms. In: Fourth international conference on communications and electronics, pp 313–317

  • Perumal S, Melekoodappattu JG (2019) ELM based detection of microcalcification in mammogram using GLCM features. Int J Recent Technol Eng 8:1146–1151

    Google Scholar 

  • Rampun A, Scotney B, Morrow P, Wang H, Winder J (2018) Breast density classification using local quinary patterns with various neighbourhood topologies. J Imaging 4:14

    Article  Google Scholar 

  • Shi P, Zhong J, Rampunc A, Wang H (2018) A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms. Comput Biol Med 96:178–188

    Article  Google Scholar 

  • Tavakoli N, Karimi M, Norouzi A et al (2019) Detection of abnormalities in mammograms using deep features. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01639-x

    Article  Google Scholar 

  • Thawkar S, Ingolikar R (2017) Automatic detection and classification of masses in digital mammograms. Int J Intell Eng Syst 10:65–74

    Google Scholar 

  • Wang J, Yang Y (2018) A context-sensitive deep learning approach for microcalcification detection in mammograms. Pattern Recogn 78:12–22

    Article  Google Scholar 

  • Wang S, Muhammad K, Phillips P et al (2017) Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-017-0639-5

    Article  Google Scholar 

  • Zhang X, Xie H (2009) A new approach for clustered microcalcifications detection. In: Asia Pacific conference on information processing, pp 322–325

  • Zhang YD, Pan C, Chen X, Wang F (2018) Abnormal breast identifcation by nine-layer convolutional neural network with parametric rectifed linear unit and rank-based stochastic pooling. J Comput Sci 27:57–68

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jayesh George Melekoodappattu.

Ethics declarations

Conflict of interest

The authors have no conflict of interest in submitting the manuscript to this journal.

Ethical approval

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

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Melekoodappattu, J.G., Subbian, P.S. Automated breast cancer detection using hybrid extreme learning machine classifier. J Ambient Intell Human Comput 14, 5489–5498 (2023). https://doi.org/10.1007/s12652-020-02359-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02359-3

Keywords

Navigation