Skip to main content
SpringerLink
Log in

A Novel Discrete Deep Learning–Based Cancer Classification Methodology

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Classification is one of the most well-known data mining branches used in diverse domains and fields. In the literature, many different classification techniques, such as statistical/intelligent, linear/nonlinear, fuzzy/crisp, shallow/deep, and single/hybrid, have been developed to cover data and systems with different characteristics. Intelligent classification approaches, especially deep learning classifiers, due to their unique features to provide accurate and efficient results, have recently attracted a lot of attention. However, in the learning process of the intelligent classifiers, a continuous distance-based cost function is used to estimate the connection weights, though the goal function in classification problems is discrete and using a continuous cost function in their learning process is unreasonable and inefficient. In this paper, a novel discrete learning–based methodology is proposed to estimate the connection weights of intelligent classifiers more accurately. In the proposed learning process, they are discretely adjusted and at once jumped to the target. This is in contrast to conventional continuous learning algorithms in which the connection weights are continuously adjusted and step by step near the target. In the present research, the proposed methodology is exemplarily applied to the deep neural network (DNN), which is one of the most recognized deep classification approaches, with a solid mathematical foundation and strong practical results in complex problems. Although the proposed methodology is just implemented on the DNN, it is a general methodology that can be similarly applied to other shallow and deep intelligent classification models. It can be generally demonstrated that the performance of the proposed discrete learning–based DNN (DIDNN) model, due to its consistency property, will not be worse than the conventional ones. The proposed DIDNN model is exemplarily evaluated on some well-known cancer classification benchmarks to illustrate the efficiency of the proposed model. The empirical results indicate that the proposed model outperforms the conventional versions of the selected deep approach in all data sets. Based on the performance analysis, the DIDNN model can improve the performance of the classic version by approximately 3.39%. Therefore, using this technique is an appropriate and effective alternative to conventional DNN-based models for classification purposes.

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
Fig. 6
Fig. 7

Similar content being viewed by others

Availability of Data and Material

Data is available from the corresponding author on reasonable request.

Code Availability

Code used in the study is available from the corresponding author on reasonable request.

References

  1. Jordan M, Mitchell T. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–60. https://doi.org/10.1126/science.aaa8415.

    Article  MathSciNet  MATH  Google Scholar 

  2. Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Netw. 1991;4(2):251–7. https://doi.org/10.1016/0893-6080(91)90009-T.

    Article  MathSciNet  Google Scholar 

  3. Van Der Malsburg C. Frank Rosenblatt: principles of neurodynamics: perceptrons and the theory of brain mechanisms. In: Brain theory. Berlin, Heidelberg: Springer Berlin Heidelberg; 1986. p. 245–8.

    Chapter  Google Scholar 

  4. Rumelhart D, Hinton G, Williams R. Learning internal representations by error propagation. In: Neurocomputing: foundations of research. MIT Press; 1988. p. 673–95.

    Google Scholar 

  5. Hinton G, Osindero S, Teh Y. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18(7):1527–54. https://doi.org/10.1162/neco.2006.18.7.1527.

    Article  MathSciNet  MATH  Google Scholar 

  6. Agrawal A, Choudhary A. Deep materials informatics: applications of deep learning in materials science. MRS Commun. 2019;9(3):779–92. https://doi.org/10.1557/mrc.2019.73.

    Article  Google Scholar 

  7. LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. In: Arbib MA, editor. Handbook of brain theory and neural networks. MIT Press; 1995. p. 3361.

    Google Scholar 

  8. LarochelleH, Bengio Y. Classification using discriminative restricted Boltzmann machines. presented at the Proceedings of the 25th international conference on machine learning. Helsinki, Finland; 2008. https://doi.org/10.1145/1390156.1390224

  9. Bank D, Koenigstein N, Giryes R. Autoencoders. arXiv preprint arXiv:2003. 2020;05991. https://doi.org/10.48550/arXiv.2003.05991

  10. Radosav D, Bakator M. Deep learning and medical diagnosis: a review of literature. Multimodal Technol Interact. 2018;2(3):47. https://doi.org/10.3390/mti2030047.

    Article  Google Scholar 

  11. Woźniak M, Siłka J, Wieczorek M. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput Appl. 2021;1–16.‏ https://doi.org/10.1007/s00521-021-05841-x.

  12. Ahmadi A, Kashefi M, Shahrokhi H, Nazari MA. Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes. Biomed Signal Process Control. 2021;63:102227. https://doi.org/10.1016/j.bspc.2020.102227.

    Article  Google Scholar 

  13. Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W. Deep learning to improve breast cancer detection on screening mammography. Sci Rep. 2019;9(1):1–12. https://doi.org/10.1038/s41598-019-48995-4.

    Article  Google Scholar 

  14. Iqbal A, Sharif M, Khan MA, Nisar W, Alhaisoni M. Ff-unet: a U-shaped deep convolutional neural network for multimodal biomedical image segmentation. Cogn Comput. 2022;14(4):1287–302. https://doi.org/10.1007/s12559-022-10038-y.

    Article  Google Scholar 

  15. Fallahpoor M, Chakraborty S, Heshejin MT, Chegeni H, Horry MJ, Pradhan B. Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection. Comput Biol Med. 2022;145:105464. https://doi.org/10.1016/j.compbiomed.2022.105464.

    Article  Google Scholar 

  16. ElKarami B, Alkhateeb A, Qattous H, Alshomali L, Shahrrava B. Multi-omics data integration model based on UMAP embedding and convolutional neural network. Cancer Inform. 2022;21:11769351221124204. https://doi.org/10.1177/11769351221124205.

    Article  Google Scholar 

  17. Zhou L, Rueda M, Alkhateeb A. Classification of breast cancer nottingham prognostic index using high-dimensional embedding and residual neural network. Cancers. 2022;14(4):934. https://doi.org/10.3390/cancers14040934.

    Article  Google Scholar 

  18. Dhungel N, Carneiro G, Bradley A. A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal. 2017;37:114–28. https://doi.org/10.1016/j.media.2017.01.009.

    Article  Google Scholar 

  19. Fakoor R, Ladhak F, Nazi A, Huber M. Using deep learning to enhance cancer diagnosis and classification. Proceedings of the 30 th International Conference on Machine Learning, Atlanta, Georgia, USA. 2013. JMLR: W&CP.

  20. Danaee P, Ghaeini R, Hendrix DA. A deep learning approach for cancer detection and relevant gene identification. Pac Symp Biocomput. 2017;22:219–29. https://doi.org/10.1142/9789813207813-0022.

    Article  Google Scholar 

  21. Wei B, Han Z, He X, Yin Y.Deep learning model based breast cancer histopathological image classification. 2017;348–353.

  22. Li C, Wang X, Liu W, Latecki L. DeepMitosis: mitosis detection via deep detection, verification and segmentation networks. Med Image Anal. 2018;45:121–33. https://doi.org/10.1016/j.media.2017.12.002.

    Article  Google Scholar 

  23. Mohaiminul Islam M, Huang S, Ajwad R, Chi C, Wang Y, Hu P. An integrative deep learning framework for classifying molecular subtypes of breast cancer. Comput Struct Biotechnol J. 2020;18:2185–99. https://doi.org/10.1016/j.csbj.2020.08.005.

    Article  Google Scholar 

  24. Li J, et al. Cervical cell multi-classification algorithm using global context information and attention mechanism. Tissue Cell. 2022;74:10167710. https://doi.org/10.1016/j.csbj.2020.08.005.

    Article  Google Scholar 

  25. Dongyao Jia A, Zhengyi Li B, Chuanwang Zhang C. Detection of cervical cancer cells based on strong feature CNN-SVM network. Neurocomputing. 2020;411:112–27. https://doi.org/10.1016/j.neucom.2020.06.006.

    Article  Google Scholar 

  26. Yaman O, Tuncer T. Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images. Biomed Signal Process Control. 2022;73:103428. https://doi.org/10.1016/j.bspc.2021.103428.

    Article  Google Scholar 

  27. Devi M, Ravi S, Vaishnavi J, Punitha S. Classification of cervical cancer using artificial neural networks. Procedia Comput Sci. 2016;89:465–72. https://doi.org/10.1016/j.procs.2016.06.105.

    Article  Google Scholar 

  28. Alyafeai Z, Ghouti L. A fully-automated deep learning pipeline for cervical cancer classification. Expert Syst Appl. 2020;141:112951. https://doi.org/10.1016/j.eswa.2019.112951.

    Article  Google Scholar 

  29. Ghoneim A, Muhammad G, Hossain M. Cervical cancer classification using convolutional neural networks and extreme learning machines. Futur Gener Comput Syst. 2020;102:643–9. https://doi.org/10.1016/j.future.2019.09.015.

    Article  Google Scholar 

  30. Lin Z, Gao Z, Ji H, Zhai R, Shen X, Mei T. Classification of cervical cells leveraging simultaneous super-resolution and ordinal regression. Appl Soft Comput. 2022;115:108208. https://doi.org/10.1016/j.asoc.2021.108208.

    Article  Google Scholar 

  31. Adem K, Kiliçarslan S, Cömert O. Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Syst Appl. 2019;115:557–64. https://doi.org/10.1016/j.eswa.2018.08.050.

    Article  Google Scholar 

  32. Ali M, et al. Machine learning-based statistical analysis for early stage detection of cervical cancer. Comput Biol Med. 2021;139:104985. https://doi.org/10.1016/j.compbiomed.2021.104985.

    Article  Google Scholar 

  33. Chen X, et al. Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients. Comput Methods Programs Biomed. 2020;197:105759. https://doi.org/10.1016/j.cmpb.2020.105759.

    Article  Google Scholar 

  34. Lu J, Song E, Ghoneim A, Alrashoud M. Machine learning for assisting cervical cancer diagnosis: an ensemble approach. Futur Gener Comput Syst. 2020;106:199–205. https://doi.org/10.1016/j.future.2019.12.033.

    Article  Google Scholar 

  35. Lozano R, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2095–128. https://doi.org/10.1016/S0140-6736(12)61728-0.

    Article  Google Scholar 

  36. Nanglia P, Kumar S, Mahajan A, Singh P, Rathee D. A hybrid algorithm for lung cancer classification using SVM and Neural Networks. ICT Express. 2021;7(3):335–41. https://doi.org/10.1016/j.icte.2020.06.007.

    Article  Google Scholar 

  37. Al-Shabi M, Shak K, Tan M. ProCAN: Progressive growing channel attentive non-local network for lung nodule classification. Pattern Recognit. 2022;122:108309. https://doi.org/10.1016/j.patcog.2021.108309.

    Article  Google Scholar 

  38. Mohanty L, Ramirez G. Optimal deep learning model for classification of lung cancer on CT images. Futur Gener Comput Syst. 2019;92:374–82. https://doi.org/10.1016/j.future.2018.10.009.

    Article  Google Scholar 

  39. Naeem Abid MM, Zia T, Ghafoor M, Windridge D. Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing. 2021;453:299–311. https://doi.org/10.1016/j.neucom.2020.06.144.

    Article  Google Scholar 

  40. Karthiga B, Rekha M. Feature extraction and I-NB classification of CT images for early lung cancer detection.  Mater Today: Proc. 2020;33:3334–41. https://doi.org/10.1016/j.matpr.2020.04.896.

    Article  Google Scholar 

  41. Bhuvaneswari P, Therese A. Detection of cancer in lung with K-NN classification using genetic algorithm. Procedia Mater Sci. 2015;10:433–40. https://doi.org/10.1016/j.mspro.2015.06.077.

    Article  Google Scholar 

  42. Zhou H, et al. Diagnosis of distant metastasis of lung cancer: based on clinical and radiomic features. Transl Oncol. 2018;11(1):31–6. https://doi.org/10.1016/j.tranon.2017.10.010.

    Article  Google Scholar 

  43. Lee A, To C, Lee A, Li J, Chan R. Model architecture and tile size selection for convolutional neural network training for non-small cell lung cancer detection on whole slide images. Inform Med Unlocked. 2022;28:100850. https://doi.org/10.1016/j.imu.2022.100850.

    Article  Google Scholar 

  44. BharatiS, Podder P, Mondal R, Mahmood A, Raihan-Al-Masud M. Comparative performance analysis of different classification algorithm for the purpose of prediction of lung cancer. Int Conf Intell Des Syst Appl. 2018;447–457.

  45. Radhika P, Nair R, Veena G. A comparative study of lung cancer detection using machine learning algorithms. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). 2019;1–4.

  46. Bartfay E, Mackillop W, Pater J. Comparing the predictive value of neural network models to logistic regression models on the risk of death for small-cell lung cancer patients. Eur J Cancer Care. 2006;15(2):115–24. https://doi.org/10.1111/j.1365-2354.2005.00638.x.

    Article  Google Scholar 

  47. Alzubi J, Bharathikannan B, Tanwar S, Manikandan R, Khanna A, Thaventhiran C. Boosted neural network ensemble classification for lung cancer disease diagnosis. Appl Soft Comput. 2019;80:579–91. https://doi.org/10.1016/j.asoc.2019.04.031.

    Article  Google Scholar 

  48. Jiao Y, Yuan J, Qiang Y, Fei S. Deep embeddings and logistic regression for rapid active learning in histopathological images. Comput Methods Programs Biomed. 2021;212:106464. https://doi.org/10.1016/j.cmpb.2021.106464.

    Article  Google Scholar 

  49. Khashei M, Bijari M. An artificial neural network (p, d, q) model for time series forecasting. Expert Syst Appl. 2010;37(1):479–89. https://doi.org/10.1016/j.eswa.2009.05.044.

    Article  MATH  Google Scholar 

  50. Rumelhart D, McClelland J. Parallel distributed processing. Cambridge, MA: MIT Press; 1986.

    Book  Google Scholar 

  51. Khashei M, Zeinal Hamadani A, Bijari M. A fuzzy intelligent approach to the classification problem in gene expression data analysis. Knowl-Based Syst. 2012;27:465–74. https://doi.org/10.1016/j.knosys.2011.10.012.

    Article  Google Scholar 

  52. Khashei M, Bijari M. A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput. 2011;11(2):2664–75. https://doi.org/10.1016/j.asoc.2010.10.015.

    Article  Google Scholar 

  53. Khashei M. Soft intelligent decision making. Ph.D. Thesis, Isfahan University of Thecnology, Deparrtment of Industrial and Systems Engineering. 2012.

  54. Hajirahimi Z, Khashei M. Hybridization of hybrid structures for time series forecasting: a review. Artif Intell Rev. 2022;1–61. https://doi.org/10.1007/s10462-022-10199-0.

  55. DuaD, Graff C. UCI machine learning repository. http://archive.ics.uci.edu/ml . Irvine, CA: University of California, School of Information and Computer Science. 2019.

  56. Sellors J, Sankaranarayanan R. Colposcopy and treatment of cervical intraepithelial neoplasia: a beginner's manual. Diamond Pocket Books (P) Ltd. 2003.

  57. Bengtsson E, Malm P. Screening for cervical cancer using automated analysis of PAP-smears. Comput Math Methods Med. 2014;2014. https://doi.org/10.1155/2014/842037.

  58. Fernandes K, Cardoso J, Fernandes J. Automated methods for the decision support of cervical cancer screening using digital colposcopies. IEEE Access. 2018;6:33910–27. https://doi.org/10.1109/ACCESS.2018.2839338.

    Article  Google Scholar 

  59. Ghanem S, Couturier R, Gregori PM. An accurate and easy to interpret binary classifier based on association rules using implication intensity and majority vote. Mathematics. 2021;9(12):1315. https://doi.org/10.3390/math9121315.

    Article  Google Scholar 

  60. Yang S, Li H, Gou X, Bian C, Shao Q. Optimized Bayesian adaptive resonance theory mapping model using a rational quadratic kernel and Bayesian quadratic regularization. Applied Intell. 2021;1–16. https://doi.org/10.1007/s10489-021-02883-5.

  61. Akter L, Islam M, Al-Rakhami M, Haque M. Prediction of cervical cancer from behavior risk using machine learning techniques. SN Comput Sci. 2021;2(3):1–10. https://doi.org/10.1007/s42979-021-00551-6.

    Article  Google Scholar 

  62. Curia F. Cervical cancer risk prediction with robust ensemble and explainable black boxes method. Health Technol. 2021;1–11. https://doi.org/10.1007/s12553-021-00554-6.

  63. Wu W, Zhou H. Data-driven diagnosis of cervical cancer with support vector machine-based approaches. IEEE Access. 2017;5:25189–95. https://doi.org/10.1109/ACCESS.2017.2763984.

    Article  Google Scholar 

  64. Adem K, Kilicarslan S, Comert O. Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder. Expert Syst Appl. 2019. https://doi.org/10.1016/j.eswa.2018.08.050.

    Article  Google Scholar 

  65. Abdoh S, Rizka M, Maghraby F. Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques. IEEE Access. 2018;6:59475–85. https://doi.org/10.1109/ACCESS.2018.2874063.

    Article  Google Scholar 

  66. Deng X, Luo Y, Wang C. Analysis of risk factors for cervical cancer based on machine learning methods. 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). 2018;23:631–635.https://doi.org/10.1109/CCIS.2018.8691126.

  67. Aslam I, Khan, N, Alshehri R, Alzahrani S, Alghamdi M, Almalki A, Balabeed M. Cervical cancer diagnosis model using extreme gradient boosting and bioinspired firefly optimization. Sci Program. 2021;2021. https://doi.org/10.1155/2021/5540024.

  68. Hsu C, Chen X, Lin W, Jiang C, Zhang Y, Hao Z, Chung Y. Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning. Measurement. 2021;175:109145. https://doi.org/10.1016/j.measurement.2021.109145.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, Iran

    Marzieh Soltani, Mehdi Khashei & Negar Bakhtiarvand

  2. Center for Optimization and Intelligent Decision Making in Healthcare Systems, COID-Health), Isfahan University of Technology (IUT), Isfahan, 8415683111, Iran

    Mehdi Khashei

Authors
  1. Marzieh Soltani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehdi Khashei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Negar Bakhtiarvand
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Khashei.

Ethics declarations

Consent to Participate

The authors declare that they have informed consent from participation and publication of this research.

Conflict of Interest

The authors declare no conflict of interests.

Research Involving Human Participants and/or Animals

The authors declare that this research does not involve human participants and/or animals.

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

Soltani, M., Khashei, M. & Bakhtiarvand, N. A Novel Discrete Deep Learning–Based Cancer Classification Methodology. Cogn Comput (2023). https://doi.org/10.1007/s12559-023-10170-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12559-023-10170-3

Keywords

Search

Navigation