Skip to main content

Advertisement

SpringerLink
Log in

An Insight into Machine Learning Techniques for Cancer Detection

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

Abstract

Cancer is a dreadful disease and a primary cause of death. Early cancer detection and screening are critical for enhancing survival rates. Conventional cancer diagnostic techniques have a number of disadvantages, including being more expensive, error-prone, and time-consuming. To address this issue, machine learning has been widely used in cancer detection and diagnosis, which provides several advantages over traditional cancer detection methods, including improved accuracy by providing a second opinion to the clinician, faster image interpretation, cost savings, and a reduction in the diversity of pathologists' interpretations. The rapid increase in cancer incidence and mortality rates served as the impetus for this study, which reviewed state-of-the-art machine learning techniques for cancer detection. This research aims to look at the current role of machine learning in cancer detection and diagnosis. We begin this study by providing an overview of machine learning and its various types. Second, this paper mulls over various machine learning methods and datasets that have been used for cancer detection. Third, previous works on cancer detection employing machine learning and deep learning methods are reviewed. Finally, challenges for potential future work are provided to guide the researchers.

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

Similar content being viewed by others

References

  1. J.J. Ott, A. Ullrich, A.B. Miller, The importance of early symptom recognition in the context of early detection and cancer survival. Eur. J. Cancer 5, 5–10 (2009). https://doi.org/10.1016/j.ejca.2009.08.009

    Article  Google Scholar 

  2. Cancer Facts & Figures 2022| American Cancer Society. (n.d.). https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html. Accessed Mar 12, 2023

  3. H. Al-Sahaf, Y. Bi, Q. Chen, A. Lensen, Y. Mei, Y. Sun, B. Tran, B. Xue, M. Zhang, A survey on evolutionary machine learning. J. R. Soc. N. Z. 49, 205–228 (2019). https://doi.org/10.1080/03036758.2019.1609052

    Article  Google Scholar 

  4. J.A. Cruz, D.S. Wishart, Applications of machine learning in cancer prediction and prognosis. Cancer Inf. 2, 59–77 (2006). https://doi.org/10.1177/117693510600200030

    Article  Google Scholar 

  5. F. de Arriba-Pérez, S. García-Méndez, F.J. González-Castaño, E. Costa-Montenegro, Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities. J. Amb. Intell. Human. Comput. (2022). https://doi.org/10.1007/s12652-022-03849-2

    Article  Google Scholar 

  6. N. Bhaskar, M. Suchetha, Analysis of salivary components as non-invasive biomarkers for monitoring chronic kidney disease. Int. J. Med. Eng. Inf. 12, 95–107 (2020). https://doi.org/10.1504/IJMEI.2020.106896

    Article  Google Scholar 

  7. V. Gupta, M. Mittal, V. Mittal, A. Gupta, An efficient AR modelling-based electrocardiogram signal analysis for health informatics. Int. J. Med. Eng. Inf. 14, 74–89 (2022). https://doi.org/10.1504/IJMEI.2022.119314

    Article  Google Scholar 

  8. M. Arunraj, A. Srinivasan, S.P. Arjunan, A real-time capable linear time classifier scheme for anticipated hand movements recognition from amputee subjects using surface EMG signals. IRBM 42, 277–293 (2021). https://doi.org/10.1016/j.irbm.2020.08.003

    Article  Google Scholar 

  9. M. Mortezaee, Z. Mortezaie, V. Abolghasemi, An improved SSA-based technique for EMG removal from ECG. IRBM 40, 62–68 (2019). https://doi.org/10.1016/j.irbm.2018.11.004

    Article  Google Scholar 

  10. M. Yochum, T. Bakir, R. Lepers, S. Binczak, A real time electromyostimulator linked with EMG analysis device. IRBM 34, 43–47 (2013). https://doi.org/10.1016/j.irbm.2012.12.003

    Article  Google Scholar 

  11. A. Diab, M. Hassan, B. Karlsson, C. Marque, Effect of decimation on the classification rate of non-linear analysis methods applied to uterine EMG signals. IRBM (2013). https://doi.org/10.1016/j.irbm.2013.07.010

    Article  Google Scholar 

  12. D. Alamedine, M. Khalil, C. Marque, Parameters extraction and monitoring in uterine EMG signals. Detection of preterm deliveries. IRBM (2013). https://doi.org/10.1016/j.irbm.2013.08.003

    Article  MATH  Google Scholar 

  13. V.R. Sarma Dhulipala, G.R. Kanagachidambaresan, Cardiac care assistance using self configured sensor network—a remote patient monitoring system. J. Inst. Eng. (India) Ser. B 95, 101–106 (2014). https://doi.org/10.1007/s40031-014-0084-1

    Article  Google Scholar 

  14. T.A. Retson, A.H. Besser, S. Sall, D. Golden, A. Hsiao, Machine learning and deep neural networks in thoracic and cardiovascular imaging. J. Thorac. Imaging 34, 192–201 (2019). https://doi.org/10.1097/RTI.0000000000000385

    Article  Google Scholar 

  15. J. Qiu, Q. Wu, G. Ding, Y. Xu, S. Feng, A survey of machine learning for big data processing. Eur. J. Adv. Signal Process. 2016, 1–16 (2016). https://doi.org/10.1186/s13634-016-0355-x

    Article  Google Scholar 

  16. L. Alzubaidi, J. Zhang, A.J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M.A. Fadhel, M. Al-Amidie, L. Farhan, Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 1–74 (2021). https://doi.org/10.1186/s40537-021-00444-8

    Article  Google Scholar 

  17. F.M. Talaat, S.A. Gamel, RL based hyper-parameters optimization algorithm (ROA) for convolutional neural network. J. Amb. Intell. Humaniz. Comput. 1, 11 (2022). https://doi.org/10.1007/s12652-022-03788-y

    Article  Google Scholar 

  18. L. Prabaharan, A. Raghunathan, An improved convolutional neural network for abnormality detection and segmentation from human sperm images. J. Amb. Intell. Humaniz. Comput. 12, 3341–3352 (2021). https://doi.org/10.1007/s12652-020-02773-7

    Article  Google Scholar 

  19. S.J. Berlin, M. John, Vision based human fall detection with Siamese convolutional neural networks. J. Amb. Intell. Humaniz. Comput. 13, 5751–5762 (2022). https://doi.org/10.1007/s12652-021-03250-5

    Article  Google Scholar 

  20. T. Rajasenbagam, S. Jeyanthi, J.A. Pandian, Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques. J. Amb. Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03075-2

    Article  Google Scholar 

  21. T. Saba, Recent advancement in cancer detection using machine learning: systematic survey of decades, comparisons and challenges. J. Infect. Public Health 13, 1274–1289 (2020). https://doi.org/10.1016/j.jiph.2020.06.033

    Article  Google Scholar 

  22. H.O. Alanazi, A.H. Abdullah, K.N. Qureshi, A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J. Med. Syst. 41, 1–10 (2017). https://doi.org/10.1007/s10916-017-0715-6

    Article  Google Scholar 

  23. S. Pitoglou, Machine learning in healthcare. J. Biol. Today’s World 8, 92–109 (2019). https://doi.org/10.4018/978-1-7998-2390-2.ch004

    Article  Google Scholar 

  24. I. Ibrahim, A. Abdulazeez, The role of machine learning algorithms for diagnosing diseases. J. Appl. Sci. Technol. Trends 2, 10–19 (2021). https://doi.org/10.38094/jastt20179

    Article  Google Scholar 

  25. M. Fatima, M. Pasha, Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 09, 1–16 (2017). https://doi.org/10.4236/jilsa.2017.91001

    Article  Google Scholar 

  26. J.E. van Engelen, H.H. Hoos, A survey on semi-supervised learning. Mach. Learn. 109, 373–440 (2020). https://doi.org/10.1007/s10994-019-05855-6

    Article  MathSciNet  MATH  Google Scholar 

  27. D.J. Joshi, I. Kale, S. Gandewar, O. Korate, D. Patwari, S. Patil, Reinforcement learning: a survey. Adv. Intell. Syst. Comput. (2020). https://doi.org/10.1007/978-981-33-4859-2_29

    Article  Google Scholar 

  28. K. Kourou, T.P. Exarchos, K.P. Exarchos, M.V. Karamouzis, D.I. Fotiadis, Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17 (2015). https://doi.org/10.1016/j.csbj.2014.11.005

    Article  Google Scholar 

  29. M.R. Hassan, M.F. Islam, M.Z. Uddin, G. Ghoshal, M.M. Hassan, S. Huda, G. Fortino, Prostate cancer classification from ultrasound and MRI images using deep learning based explainable artificial intelligence. Futur. Gener. Comput. Syst. 127, 462–472 (2022). https://doi.org/10.1016/j.future.2021.09.030

    Article  Google Scholar 

  30. L. Zhang, L. Li, M. Tang, Y. Huan, X. Zhang, X. Zhe, A new approach to diagnosing prostate cancer through magnetic resonance imaging. Alex. Eng. J. 60, 897–904 (2021). https://doi.org/10.1016/j.aej.2020.10.018

    Article  Google Scholar 

  31. Z. Wang, M. Li, H. Wang, H. Jiang, Y. Yao, H. Zhang, J. Xin, Breast cancer detection using extreme learning machine based on feature Fusion with CNN deep features. IEEE Access 7, 105146–105158 (2019). https://doi.org/10.1109/ACCESS.2019.2892795

    Article  Google Scholar 

  32. H.H. Patel, P. Prajapati, Study and analysis of decision tree based classification algorithms. Int. J. Comput. Sci. Eng. 6, 74–78 (2018). https://doi.org/10.26438/ijcse/v6i10.7478

    Article  Google Scholar 

  33. A. Higa, Diagnosis of breast cancer using decision tree and artificial neural network algorithms. Int. J. Comput. Appl. Technol. Res. 7, 23–27 (2018). https://doi.org/10.7753/ijcatr0701.1004

    Article  Google Scholar 

  34. S. Yoo, I. Gujrathi, M.A. Haider, F. Khalvati, Prostate cancer detection using deep convolutional neural networks. Sci. Rep. 9, 1–10 (2019). https://doi.org/10.1038/s41598-019-55972-4

    Article  Google Scholar 

  35. T. Saba, A. Sameh Mohamed, M. El-Affendi, J. Amin, M. Sharif, Brain tumor detection using fusion of hand crafted and deep learning features. Cognit. Syst. Res. 59, 221–230 (2020). https://doi.org/10.1016/j.cogsys.2019.09.007

    Article  Google Scholar 

  36. I. Rish, An empirical study of the naive Bayes classifier. IJCAI 2001 Worksh Empir Methods Artif Intell 3, 41–46 (2001). https://doi.org/10.1039/b104835j

    Article  Google Scholar 

  37. V.P.C. Magboo, M.S. Magboo, Machine learning classifiers on breast cancer recurrences. Procedia Comput. Sci. 2742, 2752 (2021). https://doi.org/10.1016/j.procs.2021.09.044

    Article  Google Scholar 

  38. S. Sharma, A. Aggarwal, T. Choudhury, Breast cancer detection using machine learning algorithms. Proc. Int. Conf. Comput. Tech. Electron. Mech. Syst. CTEMS 2018, 114–118 (2018). https://doi.org/10.1109/CTEMS.2018.8769187

    Article  Google Scholar 

  39. R. Gupta, A. Sarwar, V. Sharma, Screening of cervical cancer by artificial intelligence based analysis of digitized papanicolaou-smear images. Int. J. Contemp. Med. Res. 4, 2454–7379 (2017)

    Google Scholar 

  40. S. Omar, A. Ngadi, H.H. Jebur, Machine Learning techniques for anomaly detection: an overview. Int. J. Comput. Appl. 79, 33–41 (2013). https://doi.org/10.5120/13715-1478

    Article  Google Scholar 

  41. A.B. Hassanat, M.A. Abbadi, G.A. Altarawneh, A.A. Alhasanat, Solving the problem of the K parameter in the KNN classifier using an ensemble. Learn. Approach 12, 33–39 (2014)

    Google Scholar 

  42. G. Guo, H. Wang, D. Bell, Y. Bi, K. Greer, KNN model-based approach in classification, in OTM Confederated International Conferences" On the Move to Meaningful Internet Systems (2003), pp. 986–996. https://doi.org/10.1007/978-3-540-39964-3_62

  43. D. Das, L.B. Mahanta, S. Ahmed, B.K. Baishya, I. Haque, Automated classification of childhood brain tumours based on texture feature. Songklanakarin J. Sci. Technol. 41, 1014–1020 (2019). https://doi.org/10.14456/sjst-psu.2019.128

    Article  Google Scholar 

  44. V. Wasule, P. Sonar, Classification of brain MRI using SVM and KNN classifier, in Proceedings of 2017 3rd IEEE International Conference on Sensing, Signal Processing and Security, ICSSS 2017, (2017), pp. 218–223. https://doi.org/10.1109/SSPS.2017.8071594

  45. G. Song, Sentiment analysis of Japanese text and vocabulary learning based on natural language processing and SVM. J. Amb. Intelli. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03040-z

    Article  Google Scholar 

  46. S.K. Ramachandran, P. Manikandan, An efficient ALO-based ensemble classification algorithm for medical big data processing. Int. J. Med. Eng. Inf. 13, 54–63 (2021). https://doi.org/10.1504/IJMEI.2021.111864

    Article  Google Scholar 

  47. G. Biau, E. Scornet, Rejoinder on: a random forest guided tour. TEST 25, 264–268 (2016). https://doi.org/10.1007/s11749-016-0488-0

    Article  MathSciNet  MATH  Google Scholar 

  48. A.T. Azar, H.I. Elshazly, A.E. Hassanien, A.M. Elkorany, A random forest classifier for lymph diseases. Comput. Methods Prog. Biomed. 113, 465–473 (2014). https://doi.org/10.1016/j.cmpb.2013.11.004

    Article  Google Scholar 

  49. L. Rokach, Decision forest: twenty years of research. Inf. Fusion 27, 111–125 (2016). https://doi.org/10.1016/j.inffus.2015.06.005

    Article  Google Scholar 

  50. S. Bhatia, Y. Sinha, L. Goel, Lung cancer detection: a deep learning approach. Adv. Intell. Syst. Comput. 817, 699–705 (2019). https://doi.org/10.1007/978-981-13-1595-4_55

    Article  Google Scholar 

  51. S. Dasariraju, M. Huo, S. McCalla, Detection and classification of immature leukocytes for diagnosis of acute myeloid leukemia using random forest algorithm. Bioengineering 7, 1–12 (2020). https://doi.org/10.3390/bioengineering7040120

    Article  Google Scholar 

  52. M.M. Ali, K. Ahmed, F.M. Bui, B.K. Paul, S.M. Ibrahim, J.M.W. Quinn, M.A. Moni, Machine learning-based statistical analysis for early stage detection of cervical cancer. Comput. Biol. Med. 139, 104985 (2021). https://doi.org/10.1016/j.compbiomed.2021.104985

    Article  Google Scholar 

  53. N.Y. Sattar, U.A. Syed, S. Muhammad, Z. Kausar, Real-time EMG signal processing with implementation of PID control for upper-limb prosthesis. IEEE ASME Int. Conf. Adv. Intell. Mechatron AIM (2019). https://doi.org/10.1109/AIM.2019.8868796

    Article  Google Scholar 

  54. S. Makaju, P.W.C. Prasad, A. Alsadoon, A.K. Singh, A. Elchouemi, Lung cancer detection using CT scan Images. Procedia Comput. Sci. 125, 107–114 (2018). https://doi.org/10.1016/j.procs.2017.12.016

    Article  Google Scholar 

  55. A.F.M. Agarap, On breast cancer detection: an application of machine learning algorithms on the Wisconsin diagnostic dataset. ACM Int. Conf. Proc. Ser. (2018). https://doi.org/10.1145/3184066.3184080

    Article  Google Scholar 

  56. S. Acharya, A. Alsadoon, P.W.C. Prasad, S. Abdullah, A. Deva, Deep convolutional network for breast cancer classification: enhanced loss function (ELF). J. Supercomput. 76, 8548–8565 (2020). https://doi.org/10.1007/s11227-020-03157-6

    Article  Google Scholar 

  57. A. Kumar, S.K. Singh, S. Saxena, K. Lakshmanan, A.K. Sangaiah, H. Chauhan, S. Shrivastava, R.K. Singh, Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer. Inf. Sci. 508, 405–421 (2020). https://doi.org/10.1016/j.ins.2019.08.072

    Article  Google Scholar 

  58. K. Balasubramanian, N.P. Ananthamoorthy, Robust retinal blood vessel segmentation using convolutional neural network and support vector machine. J. Amb. Intell. Humaniz. Comput. 12, 3559–3569 (2021). https://doi.org/10.1007/s12652-019-01559-w

    Article  Google Scholar 

  59. K.B. Vaishnavee, K. Amshakala, An automated MRI brain image segmentation and tumor detection using SOM-clustering and Proximal Support Vector Machine classifier, in ICETECH 2015–2015 IEEE International Conference on Engineering and Technology, (2015), pp. 1–6. https://doi.org/10.1109/ICETECH.2015.7275030.

  60. A. Hamza & H. Moetque, Diabetes Disease Diagnosis Method based on Feature Extraction using K-SVM. International Journal of Advanced Computer Science and Applications, 8 (2017) 236–244. https://doi.org/10.14569/ijacsa.2017.080130.

  61. M. Telgarsky, A. Vattani, Hartigan’s method: κ-means clustering without Voronoi. J. Mach. Learn. Res. 9, 820–827 (2010)

    Google Scholar 

  62. R.T. Aldahdooh, W. Ashour, DIMK-means “Distance-based Initialization method for K-means clustering algorithm.” Int. J. Intell. Syst. Appl. 5, 41–51 (2013). https://doi.org/10.5815/ijisa.2013.02.05

    Article  Google Scholar 

  63. M. Toğaçar, Detection of segmented uterine cancer images by hotspot detection method using deep learning models, pigeon-inspired optimization, types-based dominant activation selection approaches. Comput. Biol. Med. 136, 104659 (2021). https://doi.org/10.1016/j.compbiomed.2021.104659

    Article  Google Scholar 

  64. D.A. Bashar, Survey on evolving deep learning neural network architectures. J. Artif. Intell. Capsule Netw. 2019, 73–82 (2019). https://doi.org/10.36548/jaicn.2019.2.003

    Article  Google Scholar 

  65. A. Qayyum, J. Qadir, M. Bilal, A. Al-Fuqaha, Secure and robust machine learning for healthcare: a survey. IEEE Rev. Biomed. Eng. 14, 156–180 (2021). https://doi.org/10.1109/RBME.2020.3013489

    Article  Google Scholar 

  66. R. Yamashita, M. Nishio, R.K.G. Do, K. Togashi, Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9

    Article  Google Scholar 

  67. J. Ker, L. Wang, J. Rao, T. Lim, Deep learning applications in medical image analysis. IEEE Access 6, 9375–9379 (2017). https://doi.org/10.1109/ACCESS.2017.2788044

    Article  Google Scholar 

  68. S. Liu, Y. Wang, X. Yang, B. Lei, L. Liu, S.X. Li, D. Ni, T. Wang, Deep learning in medical ultrasound analysis: a review. Engineering 5, 261–275 (2019). https://doi.org/10.1016/j.eng.2018.11.020

    Article  Google Scholar 

  69. K. Vivekanandan, N. Praveena, Hybrid convolutional neural network (CNN) and long-short term memory (LSTM) based deep learning model for detecting shilling attack in the social-aware network. J. Amb. Intell. Humaniz. Comput. 12, 1197–1210 (2021). https://doi.org/10.1007/s12652-020-02164-y

    Article  Google Scholar 

  70. S. Iqbal, M.U. Ghani Khan, T. Saba, Z. Mehmood, N. Javaid, A. Rehman, R. Abbasi, Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation. Microsc. Res. Tech. 82, 1302–1315 (2019). https://doi.org/10.1002/jemt.23281

    Article  Google Scholar 

  71. M.A. Rezaei, A. Fathollahi, S. Rezaei, J. Hu, M. Gheisarnejad, A.R. Teimouri, R. Rituraj, A.H. Mosavi, M.H. Khooban, Adaptation of a real-time deep learning approach with an analog fault detection technique for reliability forecasting of capacitor banks used in mobile vehicles. IEEE Access 10, 132271–132287 (2022). https://doi.org/10.1109/ACCESS.2022.3228916

    Article  Google Scholar 

  72. T. Saba, M.A. Khan, A. Rehman, S.L. Marie-Sainte, Region extraction and classification of skin cancer: a heterogeneous framework of deep CNN features fusion and reduction. J. Med. Syst. 43, 1–19 (2019). https://doi.org/10.1007/s10916-019-1413-3

    Article  Google Scholar 

  73. D.R. Nayak, R. Dash, B. Majhi, R.B. Pachori, Y. Zhang, A deep stacked random vector functional link network autoencoder for diagnosis of brain abnormalities and breast cancer. Biomed. Signal Process. Control 58, 101860 (2020). https://doi.org/10.1016/j.bspc.2020.101860

    Article  Google Scholar 

  74. A. Ghoneim, G. Muhammad, M.S. Hossain, Cervical cancer classification using convolutional neural networks and extreme learning machines. Futur. Gener. Comput. Syst. 102, 643–649 (2020). https://doi.org/10.1016/j.future.2019.09.015

    Article  Google Scholar 

  75. K. Suzuki, Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10, 257–273 (2017). https://doi.org/10.1007/s12194-017-0406-5

    Article  Google Scholar 

  76. Z. Hu, J. Tang, Z. Wang, K. Zhang, L. Zhang, Q. Sun, Deep learning for image-based cancer detection and diagnosis—a survey. Pattern Recogn. 83, 134–149 (2018). https://doi.org/10.1016/j.patcog.2018.05.014

    Article  Google Scholar 

  77. L. Alzubaidi, M.A. Fadhel, O. Al-Shamma, J. Zhang, J. Santamaría, Y. Duan, S.R. Oleiwi, Towards a better understanding of transfer learning for medical imaging: a case study. Appl. Sci. (Switzerland) 10, 1–21 (2020). https://doi.org/10.3390/app10134523

    Article  Google Scholar 

  78. G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A.W.M. van der Laak, B. van Ginneken, C.I. Sánchez, A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017). https://doi.org/10.1016/j.media.2017.07.005

    Article  Google Scholar 

  79. V.N. Gopal, F. Al-Turjman, R. Kumar, L. Anand, M. Rajesh, Feature selection and classification in breast cancer prediction using IoT and machine learning. Meas. J. Int. Meas. Confeder. 178, 109442 (2021). https://doi.org/10.1016/j.measurement.2021.109442

    Article  Google Scholar 

  80. S.L. Aarthy, S. Prabu, Classification of breast cancer based on thermal image using support vector machine. Int. J. Bioinform. Res. Appl. 15, 51–67 (2019). https://doi.org/10.1504/IJBRA.2019.097997

    Article  Google Scholar 

  81. S.R. Sannasi Chakravarthy, H. Rajaguru, Automatic Detection and classification of mammograms using improved extreme learning machine with deep learning. Irbm 43, 49–61 (2021). https://doi.org/10.1016/j.irbm.2020.12.004

    Article  Google Scholar 

  82. S.P.A. Surendhar, R. Vasuki, Breast cancers detection using deep learning algorithm. Mater. Today Proc. (2021). https://doi.org/10.1016/j.matpr.2020.11.600

    Article  Google Scholar 

  83. S.K. Gopalan, A. Natarajan, N. Mathappan, R.S. Soundariya, Bio-medical analysis of breast cancer risk detection based on deep neural network. Int. J. Med. Eng. Inf. 12, 529 (2020). https://doi.org/10.1504/ijmei.2020.10032878

    Article  Google Scholar 

  84. A.R. Vaka, B. Soni, R.K. Sudheer, Breast cancer detection by leveraging machine learning. ICT Express 6, 320–324 (2020). https://doi.org/10.1016/j.icte.2020.04.009

    Article  Google Scholar 

  85. M. Amraoui, T.B. Stambouli, B. Alshaqaqi, On using the wisdom of the crowd principles in classification, application on breast cancer diagnosis and prognosis. Int. J. Bioinform. Res. Appl. 15, 324–346 (2019). https://doi.org/10.1504/IJBRA.2019.103784

    Article  Google Scholar 

  86. M.M. Rahman, Y. Ghasemi, E. Suley, Y. Zhou, S. Wang, J. Rogers, Machine learning based computer aided diagnosis of breast cancer utilizing anthropometric and clinical features. IRBM 42, 215–226 (2021). https://doi.org/10.1016/j.irbm.2020.05.005

    Article  Google Scholar 

  87. S.B. Akben, Determination of the blood, hormone and obesity value ranges that indicate the breast cancer, using data mining based expert system. IRBM 40, 355–360 (2019). https://doi.org/10.1016/j.irbm.2019.05.007

    Article  Google Scholar 

  88. T. Liu, J. Huang, T. Liao, R. Pu, S. Liu, Y. Peng, A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data. IRBM 43, 62–74 (2022). https://doi.org/10.1016/j.irbm.2020.12.002

    Article  Google Scholar 

  89. A. Belderrar, A. Hazzab, Real-time estimation of hospital discharge using fuzzy radial basis function network and electronic health record data. Int. J. Med. Eng. Inf. 13, 75–83 (2021). https://doi.org/10.1504/IJMEI.2021.111870

    Article  Google Scholar 

  90. F. Ramesh Dhanaseelan, M. Jeya Sutha, Detection of breast cancer based on fuzzy frequent itemsets mining. IRBM 42, 198–206 (2021). https://doi.org/10.1016/j.irbm.2020.05.002

    Article  Google Scholar 

  91. P. Kumar Mallick, S.H. Ryu, S.K. Satapathy, S. Mishra, G.N. Nguyen, P. Tiwari, Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 7, 46278–46287 (2019). https://doi.org/10.1109/ACCESS.2019.2902252

    Article  Google Scholar 

  92. R. Patil, S. Bellary, Machine learning approach in melanoma cancer stage detection. J. King Saud Univ. Comput. Inf. Sci. (2020). https://doi.org/10.1016/j.jksuci.2020.09.002

    Article  Google Scholar 

  93. Z. Waheed, A. Waheed, M. Zafar, F. Riaz, An efficient machine learning approach for the detection of melanoma using dermoscopic images. in Proceedings of the 2017 Internatinal Conference on Communication Computer Digital Systems C-CODE 2017 (2017), pp. 316–319. https://doi.org/10.1109/C-CODE.2017.7918949

  94. A. Rehman, M.A. Khan, Z. Mehmood, T. Saba, M. Sardaraz, M. Rashid, Microscopic melanoma detection and classification: a framework of pixel-based fusion and multilevel features reduction. Microsc. Res. Tech. 83, 410–423 (2020). https://doi.org/10.1002/jemt.23429

    Article  Google Scholar 

  95. A. Das, U.R. Acharya, S.S. Panda, S. Sabut, Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cogn. Syst. Res. 54, 165–175 (2019). https://doi.org/10.1016/j.cogsys.2018.12.009

    Article  Google Scholar 

  96. A. Ben-Cohen, I. Diamant, E. Klang, M. Amitai, H. Greenspan, Fully convolutional network for liver segmentation and lesions detection, in Deep Learning and Data Labeling for Medical Applications, (2016), pp. 77–85. https://doi.org/10.1007/978-3-319-46976-8_9

  97. C.C. Chang, H.H. Chen, Y.C. Chang, M.Y. Yang, C.M. Lo, W.C. Ko, Y.F. Lee, K.L. Liu, R.F. Chang, Computer-aided diagnosis of liver tumors on computed tomography images. Comput. Methods Prog. Biomed. 145, 45–51 (2017). https://doi.org/10.1016/j.cmpb.2017.04.008

    Article  Google Scholar 

  98. M.J.A. Jansen, H.J. Kuijf, W.B. Veldhuis, F.J. Wessels, M.A. Viergever, J.P.W. Pluim, Automatic classification of focal liver lesions based on MRI and risk factors. PLoS ONE 14, 1–13 (2019). https://doi.org/10.1371/journal.pone.0217053

    Article  Google Scholar 

  99. L. Zhang, L. Lu, I. Nogues, R.M. Summers, S. Liu, J. Yao, DeepPap: Deep convolutional networks for cervical cell classification. IEEE J. Biomed. Health Inform. 21, 1633–1643 (2017). https://doi.org/10.1109/JBHI.2017.2705583

    Article  Google Scholar 

  100. A. Asuntha, A. Srinivasan, Deep learning for lung cancer detection and classification. Multimedia Tools Appl. 79, 7731–7762 (2020). https://doi.org/10.1007/s11042-019-08394-3

    Article  Google Scholar 

  101. A. Rehman, N. Abbas, T. Saba, S. I. ur Rahman, Z. Mehmood, & H. Kolivand, Classification of acute lymphoblastic leukemia using deep learning. Microscopy Res. Tech. 81, 1310–1317 (2018). https://doi.org/10.1002/jemt.23139

    Article  Google Scholar 

  102. F. Kazemi, T. Najafabadi, B. Araabi, Automatic recognition of acute myelogenous leukemia in blood microscopic images using K-means clustering and support vector machine. J. Med. Signals Sens. 6, 183–193 (2016). https://doi.org/10.4103/2228-7477.186885

    Article  Google Scholar 

  103. C.A. Kumar, D.M.N. Mubarak, Classification of early stages of esophageal cancer using transfer learning. Irbm 43, 251–258 (2021). https://doi.org/10.1016/j.irbm.2021.10.003

    Article  Google Scholar 

  104. D. Sarwinda, R.H. Paradisa, A. Bustamam, P. Anggia, Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Procedia Comput. Sci. 423, 431 (2021). https://doi.org/10.1016/j.procs.2021.01.025

    Article  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Computer Science Engineering, Deenbandhu Chhotu Ram University of Science And Technology, Murthal, Haryana, India

    Indu Chhillar & Ajmer Singh

Authors
  1. Indu Chhillar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajmer Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Indu Chhillar.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

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

Chhillar, I., Singh, A. An Insight into Machine Learning Techniques for Cancer Detection. J. Inst. Eng. India Ser. B 104, 963–985 (2023). https://doi.org/10.1007/s40031-023-00896-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40031-023-00896-x

Keywords

Search

Navigation