Skip to main content
SpringerLink
Log in

Automatic liver tumor detection and classification using the hyper tangent fuzzy C-Means and improved fuzzy SVM

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Globally liver diseases are the most life-threatening diseases, and according to global cancer statistics, liver cancer is the most common. Early detection of liver cancer can prevent millions of patients' mortality every year. Automatic liver cancer detection will help radiologists to determine the tumour identification and its severity, and it is also helpful to reduce the occurrence of errors which results in a reduction in the number of deaths from liver cancer. It gives more accurate results in less time, saving the radiologist's effort and time. The proposed model focused on improving the segmenting of liver images and then classifying the liver tumours from the CT images. The present study suggests the hyper tangent Fuzzy C-Means (HTFCM) to segment the liver images. It used Hyper tangent distance to calculate the data point distance from the cluster centres and obtained segmentation results almost closer to the ground truth liver images. Due to the fuzziness in the liver images, all state-of-the-art models except the proposed model cannot precisely locate the tumours. This study solved the issue of linear mapping using fuzzy logic, improved the classification accuracy, and reduced the processing time of early diagnosis of liver diseases. The proposed model improves the classification accuracy to 99.58% and reduces the processing time by 2-25 s to classify the liver tumours.

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
Algorithm 1
Fig. 5
Algorithm 2
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

Yes.

References

  1. Deshmukh SP, Choudhari D, Amalraj S, Matte PN (2023) Hybrid deep learning method for detection of liver cancer. Comput Assist Methods Eng Sci 30(2):151–65. https://doi.org/10.24423/cames.463

    Article  Google Scholar 

  2. ABOUT LIVER CANCER, https://www.Cancer.Org/Cancer/Liver-Cancer/. Accessed 20 Oct 2022

  3. Almotairi S, Kareem G, Aouf M, Almutairi B, Salem MA (2020) Liver tumor segmentation in CT scans using modified segnet. Sensors 20(5):1516. https://doi.org/10.3390/s20051516 

    Article  Google Scholar 

  4. Siegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics, 2023. Ca Cancer J Clin 73(1):17–48. https://doi.org/10.3322/Caac.21763

    Article  Google Scholar 

  5. Christ PF, Elshaer ME, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Rempfler M, Armbruster M, Hofmann F, D’Anastasi M, Sommer WH (2016) Automatic Liver And Lesion Segmentation In CT Using Cascaded Fully Convolutional Neural Networks And 3D Conditional Random Fields. In medical Image Computing And Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17–21, Proceedings, Part II 19 2016. Springer International Publishing, pp 415–423. https://doi.org/10.1007/978-3-319-46723-8_48

  6. Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Abergel A, Chabrot P, Magnin B (2019) Automatic segmentation methods for liver and hepatic vessels from CT And MRI volumes, applied to the Couinaud scheme. Comput Biol Med 1(110):42–51. https://doi.org/10.1016/j.compbiomed.2019.04.014

    Article  Google Scholar 

  7. Kozlov OV, Kondratenko YP, Skakodub OS (2023) Intelligent information technology for structural optimization of fuzzy control and decision-making systems. Inartificial Intelligence In Control And Decision-Making Systems: Dedicated To Professor Janusz Kacprzyk. Cham: Springer Nature Switzerland, pp 127–165. https://doi.org/10.1007/978-3-031-25759-9_7

  8. Acharya UR, Koh JE, Hagiwara Y, Tan JH, Gertych A, Vijayananthan A, Yaakup NA, Abdullah BJ, Fabell MK, Yeong CH (2018) Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Comput Biol Med 1(94):11–18. https://doi.org/10.1016/j.compbiomed.2017.12.024

    Article  Google Scholar 

  9. Alirr OI, Rahni AA, Golkar E (2018) An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning. Int J Comput Assist Radiol Surg 13:1169–1176. https://doi.org/10.1007/s11548-018-1801-z

    Article  Google Scholar 

  10. ShanmugaSundaram P, Santhiyakumari N (2019) An enhancement of computer aided approach for colon cancer detection in WCE images using ROI based color histogram and SVM2. J Med Syst 43(2):29. https://doi.org/10.1007/s10916-018-1153-9

    Article  Google Scholar 

  11. Das A, Acharya UR, Panda SS, Sabut S (2019) Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cogn Syst Res 1(54):165–175. https://doi.org/10.1016/j.cogsys.2018.12.009

    Article  Google Scholar 

  12. Kadoury S, Vorontsov E, Tang A (2015) Metastatic liver tumour segmentation from discriminant Grassmannian manifolds. Phys Med Biol 60(16):6459. https://doi.org/10.1088/0031-9155/60/16/6459

    Article  Google Scholar 

  13. Kaya Y, Kuncan F (2022) A hybrid model for classification of medical data set based on factor analysis and extreme learning machine: FA+ ELM. Biomed Signal Process Control 1(78):104023. https://doi.org/10.1016/j.bspc.2022.104023

    Article  Google Scholar 

  14. Devi RM, Seenivasagam V (2020) Automatic segmentation and classification of liver tumor from CT image using feature difference and SVM based classifier-soft computing technique. Soft Comput 24:18591–18598. https://doi.org/10.1007/s00500-020-05094-1

    Article  Google Scholar 

  15. Zhou S, Li D, Zhang Z, Ping R (2020) A new membership scaling fuzzy C-Means clustering algorithm. IEEE Trans Fuzzy Syst 29(9):2810–2818. https://doi.org/10.1109/tfuzz.2020.3003441

    Article  Google Scholar 

  16. Dhruv B, Mittal N, Modi M (2023) Hybrid particle swarm optimized and fuzzy C Means clustering based segmentation technique for investigation of COVID-19 infected chest CT. Comput Methods Biomech Biomed Eng: Imaging Vis 11(2):197–204. https://doi.org/10.1080/21681163.2022.2061376

    Article  Google Scholar 

  17. Dahiya S, Gosain A (2023) A novel Type-II intuitionistic fuzzy clustering algorithm for mammograms segmentation. J Ambient Intell Human Comput 14(4):3793–3808. https://doi.org/10.1007/s12652-022-04022-5

    Article  Google Scholar 

  18. Phamtoan D, Vovan T (2023) The fuzzy cluster analysis for interval value using genetic algorithm and its application in image recognition. Comput Stat 38(1):25–51. https://doi.org/10.1007/s00180-022-01215-6

    Article  MathSciNet  Google Scholar 

  19. Lits-Liver Tumor Segmentation Challenge, https://Competitions.Codalab.Org/Competitions/17094. Accessed 20 Oct 2022

  20. Ejegwa PA, Onyeke IC, Kausar N, Kattel P (2023) A new partial correlation coefficient technique based on intuitionistic fuzzy information and its pattern recognition application. Int J Intell Syst 6:2023. https://doi.org/10.1155/2023/5540085

    Article  Google Scholar 

  21. Ma P, Li C, Rahaman MM, Yao Y, Zhang J, Zou S, Zhao X, Grzegorzek M (2023) A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches. Artif Intell Rev 56(2):1627–1698. https://doi.org/10.1007/s10462-022-10209-1

    Article  Google Scholar 

  22. Mishra AP, Noida SI, Bajpai MM, Singhal MS, Tripathi MJ, Sinha MP, Bhardwaj P (2023) Adaptive non linear deblurring technique: a new image enhancement technique. Semicond Optoelectron 42(1):34–42. https://doi.org/10.1109/42.712133

    Article  Google Scholar 

  23. Jähne B (1995) Concepts, algorithms, and scientific applications. Digit Image Processing 1(1):1–320

  24. Lou C, Xie X (2023) Multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss for classification of noisy data. Neurocomputing 14:126458. https://doi.org/10.1016/j.neucom.2023.12645

    Article  Google Scholar 

  25. Yang P, Song W, Zhao X, Zheng R, Qingge L (2020) An improved Otsu threshold segmentation algorithm. Int J Comput Sci Eng 22(1):146–153. https://doi.org/10.1504/ijcse.2020.107266

    Article  Google Scholar 

  26. Bo W, Ying W, Lijie C (2020) Fuzzy clustering recognition algorithm of medical image with multi-resolution feature. Concurr Comput: Pract Exp 32(1):E4886. https://doi.org/10.1002/cpe.4886

    Article  Google Scholar 

  27. Shynu PG, Shayan HM, Chowdhary CL (2020) A fuzzy based data perturbation technique for privacy preserved data mining. In 2020 International Conference On Emerging Trends In Information Technology And Engineering (Ic-ETITE). IEEE, pp 1–4. https://doi.org/10.1109/ic-etite47903.2020.244

  28. Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy C-Means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530. https://doi.org/10.1109/tfuzz.2004.840099

    Article  Google Scholar 

  29. Chowdhary CL, Mittal M, K P, Pattanaik PA, Marszalek Z (2020) An efficient segmentation and classification system in medical images using intuitionist possibilistic fuzzy C-Mean clustering and fuzzy SVM algorithm. Sensors 20(14):3903. https://doi.org/10.3390/s20143903

    Article  Google Scholar 

  30. Ke Q, Zhang J, Wei W, Połap D, Woźniak M, Kośmider L, Damaševĭcius R (2019) A neuro-heuristic approach for recognition of lung diseases from X-Ray images. Expert Syst Appl 15(126):218–232. https://doi.org/10.1016/j.eswa.2019.01.060

    Article  Google Scholar 

  31. Ahmad M, Qadri SF, Ashraf MU, Subhi K, Khan S, Zareen SS, Qadri S (2022) Efficient liver segmentation from computed tomography images using deep learning. Comput Intell Neurosci 18:2022. https://doi.org/10.1155/2022/2665283

    Article  Google Scholar 

  32. Rahman H, Bukht TF, Imran A, Tariq J, Tu S, Alzahrani A (2022) A deep learning approach for liver and tumor segmentation in CT images using Resunet. Bioengineering 9(8):368. https://doi.org/10.3390/bioengineering9080368

    Article  Google Scholar 

  33. Appadurai JP, Kavin BP, Lai WC (2023) En–Denet based segmentation and gradational modular network classification for liver cancer diagnosis. Biomedicines 11(5):1309. https://doi.org/10.3390/biomedicines11051309

    Article  Google Scholar 

  34. Chlebus G, Meine H, Moltz JH, Schenk A (2017) Neural network-based automatic liver tumor segmentation with random forest-based candidate filtering. Arxiv Preprint Arxiv:1706.00842. https://doi.org/10.48550/arxiv.1706.00842

  35. Jansen MJ, Kuijf HJ, Veldhuis WB, Wessels FJ, Viergever MA, Pluim JP (2019) Automatic classification of focal liver lesions based on MRI and risk factors. PLoS ONE 14(5):E0217053. https://doi.org/10.1371/journal.pone.0217053

    Article  Google Scholar 

  36. Dong X, Zhou Y, Wang L, Peng J, Lou Y, Fan Y (2020) Liver cancer detection using hybridized fully convolutional neural network based on deep learning framework. IEEE Access 1(8):129889–129898. https://doi.org/10.1109/access.2020.3006362

    Article  Google Scholar 

  37. Soler L, Hostettler A, Agnus V, Charnoz A, Fasquel JB, Moreau J, Osswald AB, Bouhadjar M, Marescaux J (2010) 3D image reconstruction for comparison of algorithm database. https://www.ircad.fr/research/data-sets/liver-segmentation-3d-ircadb-01. Accessed 20 Oct 2022

  38. Othman E, Mahmoud M, Dhahri H, Abdulkader H, Mahmood A, Ibrahim M (2022) Automatic detection of liver cancer using hybrid pre-trained models. Sensors 22(14):5429. https://doi.org/10.3390/s22145429

    Article  Google Scholar 

  39. Midya A, Chakraborty J, Srouji R, Narayan RR, Boerner T, Zheng J, Pak LM, Creasy JM, Escobar LA, Harrington KA, Gönen M (2023) Computerized diagnosis of liver tumors from CT scans using a deep neural network approach. IEEE J Biomed Health Inform. https://doi.org/10.1109/jbhi.2023.3248489

    Article  Google Scholar 

  40. Amritha M, Manimegalai P (2023) Liver tumor segmentation and classification using deep learning. In 2023 Fifth International Conference On Electrical, Computer And Communication Technologies (ICECCT). IEEE, pp 01–07 https://doi.org/10.1109/icecct56650.2023.10179731

  41. Vasundhara N, Nandan AS, Hemanth SV, Macherla S, Madhura GK (2023) An efficient biomedical solicitation in liver cancer classification by deep learning approach. In 2023 IEEE International Conference On Integrated Circuits And Communication Systems (ICICACS). IEEE, pp 01–05. https://doi.org/10.1109/icicacs57338.2023.10099828

  42. Sowparnika B, Yamini K, Walid MA, Prasad J, Aparna N, Chauhan A (2023) Innovative Method For Detecting Liver Cancer Using Auto Encoder And Single Feed Forward Neural Network. In2023 2nd International Conference On Applied Artificial Intelligence And Computing (ICAAIC). IEEE, pp 156–161. https://doi.org/10.1109/icaaic56838.2023.10140207

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India

    Usharani Bhimavarapu

Authors
  1. Usharani Bhimavarapu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Usharani Bhimavarapu.

Ethics declarations

Conflict of interest

Authors do not have conflict of interest.

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

Bhimavarapu, U. Automatic liver tumor detection and classification using the hyper tangent fuzzy C-Means and improved fuzzy SVM. Multimed Tools Appl 83, 46201–46220 (2024). https://doi.org/10.1007/s11042-023-17430-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17430-2

Keywords

Search

Navigation