Skip to main content
Springer Nature Link
Log in

Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Parotid tumors are among the most prevalent tumors in otolaryngology, and malignant parotid tumors are one of the main causes of facial paralysis in patients. Currently, the main diagnostic modality for parotid tumors is computed tomography, which relies mainly on the subjective judgment of clinicians and leads to practical problems such as high workloads. Therefore, to assist physicians in solving the preoperative classification problem, a stacked generalization model is proposed for the automated classification of parotid tumor images. A ResNet50 pretrained model is used for feature extraction. The first layer of the adopted stacked generalization model consists of multiple weak learners, and the results of the weak learners are integrated as input data in a meta-classifier in the second layer. The output results of the meta-classifier are the final classification results. The classification accuracy of the stacked generalization model reaches 91%. Comparing the classification results under different classifiers, the stacked generalization model used in this study can identify benign and malignant tumors in the parotid gland effectively, thus relieving physicians of tedious work pressure.

Graphical Abstract

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /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 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

Similar content being viewed by others

References

  1. Gurung NV, Shrestha D, Acharya A, Gurung A, Shrestha S, Poudel S, Chapagain A, Regmi S (2017) Superficial parotidectomy by retrograde facial nerve dissection. J Gandaki Med Coll-Nepal 10(1):25–27

    Article  Google Scholar 

  2. Magnano M, Fernando Gervasio C, Cravero L, Machetta G, Lerda W, Beltramo G, Orecchia R, Ragona R, Bussi M (1999) Treatment of malignant neoplasms of the parotid gland. Otolaryngol-Head Neck Surg 121(5):627–632

    Article  CAS  PubMed  Google Scholar 

  3. Yerli H, Agildere AM (2008) Parotid gland tumors: advanced imaging technologies[J]. Cancer Imaging pp 563–573. https://doi.org/10.1016/B978-012374212-4.50135-8

    Chapter  Google Scholar 

  4. Xu Y, Shu Z, Song G, Liu Y, Pang P, Wen X, Gong X (2021) The role of preoperative computed tomography radiomics in distinguishing benign and malignant tumors of the parotid gland. Front Oncol 11:463

    Google Scholar 

  5. Lee CH, Lee HS, Jin SM, Lee SH, Pyo JS, Sohn JH (2011) Efficacy of fine needle aspiration biopsy in parotid gland tumors. Korean J Head Neck Oncol 27(2):204–209

    Google Scholar 

  6. Kaya G, Howlett DC (2015) The diagnosis of parotid lesions. Eur Radiol 25(10):3025–3026

    Article  PubMed  Google Scholar 

  7. Sahin E, Unlu I, Unlu EN, Kaptan Z, Uzunkulaoglu H, Samim EE, Tulaci KG, Karadavut Y (2016) Clinical importance of preoperative fine needle aspiration biopsy and computed tomography in parotid gland masses. Eur Res J 2(1):16–22

    Article  Google Scholar 

  8. Heller KS, Attie JN, Dubner S (1993) Accuracy of frozen section in the evaluation of salivary tumors. Am J Surg 166(4):424–427

    Article  CAS  PubMed  Google Scholar 

  9. Mansour N, Stock K, Chaker A, Bas M, Knopf A (2012) Evaluation of parotid gland lesions with standard ultrasound, color duplex sonography, sonoelastography, and acoustic radiation force impulse imaging–a pilot study. Ultraschall der Med-Eur J Ultrasound 33(03):283–288

    Article  CAS  Google Scholar 

  10. Aro K, Korpi J, Tarkkanen J, Mäkitie A, Atula T (2020) Preoperative evaluation and treatment consideration of parotid gland tumors. Laryngoscope Investig Otolaryngol 5(4):694–702

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gökçe E (2020) Multiparametric magnetic resonance imaging for the diagnosis and differential diagnosis of parotid gland tumors. J Magn Reson Imaging 52(1):11–32

    Article  PubMed  Google Scholar 

  12. Yuan Y, Hong Y, Liv X, Peng J, Li M, Guo D, Huang P, Chen C, Yan Z, Chen C, Li H, Ma H, Wang Y (2021) Differentiating benign and malignant parotid gland tumors using CT images and machine learning algorithms. Int J Clin Exp Med. 14(15):1864–1873

    Google Scholar 

  13. Zhang H, Lai H, Wang Y, Lv X, Hong Y, Peng J, Zhang Z, Chen C, Chen C (2021) Research on the classification of benign and malignant parotid tumors based on transfer learning and a convolutional neural network. IEEE Access 9:40360–40371

    Article  Google Scholar 

  14. Gabelloni M, Faggioni L, Attanasio S, Vani V, Goddi A, Colantonio S, Germanese D, Caudai C, Bruschini L, Scarano M (2020) Can magnetic resonance radiomics analysis discriminate parotid gland tumors? Pilot Stud Diagn 10(11):900

    Google Scholar 

  15. Yuan J, Fan Y, Lv X, Chen C, Wa Ng Y (2020) Research on the practical classification and privacy protection of CT images of parotid tumors based on ResNet50 model. J Phys: Conf Ser 1576:012040

    Google Scholar 

  16. Matsuo H, Nishio M, Kanda T, Kojita Y, Kono AK, Hori M, Teshima M, Otsuki N, Nibu K-i, Murakami T (2020) Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI. Sci Rep 10(1):1–9

    Article  Google Scholar 

  17. Chang YJ, Huang TY, Liu YJ, Chung HW, Juan CJ (2021) Classification of parotid gland tumors by using multimodal MRI and deep learning. NMR Biomed 34(1):e4408

    Article  PubMed  Google Scholar 

  18. Shao S, Zheng N, Mao N, Xue X, Cui J, Gao P, Wang B (2021) A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging. Clin Radiol 76(6):472. e411-472. e418

    Article  Google Scholar 

  19. Zheng Y-m, Xu W-j, Hao D-p, Liu X-j, Gao C-p, Tang G-z, Li J, Wang H-x, Dong C (2021) A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland. Eur Radiol 31(5):2886–2895

    Article  PubMed  Google Scholar 

  20. Wachinger C, Brennan M, Sharp GC, Golland P (2016) Efficient descriptor-based segmentation of parotid glands with nonlocal means. IEEE Trans Biomed Eng 64(7):1492–1502

    Article  PubMed  PubMed Central  Google Scholar 

  21. Nielsen MA (2015) Neural networks and deep learning, vol 25. Determination press San Francisco, CA

    Google Scholar 

  22. Salama WM, Elbagoury AM, Aly MH (2020) Novel breast cancer classification framework based on deep learning. IET Image Proc 14(13):3254–3259

    Article  Google Scholar 

  23. Elpeltagy M, Sallam H (2021) Automatic prediction of COVID− 19 from chest images using modified ResNet50. Multimedia Tools and Applications:1–13

  24. Mishra NK, Singh P, Joshi SD (2021) Automated detection of COVID-19 from CT scan using convolutional neural network. Biocybernetics Biomed Eng 41(2):572–588

    Article  Google Scholar 

  25. Liu Y, Pu H, Sun DW (2021) Efficient extraction of deep image features using convolutional neural network (CNN) for applications in detecting and analysing complex food matrices[J]. Trends Food Sci Technol 113(7). https://doi.org/10.1016/j.tifs.2021.04.042

  26. Franch G, Nerini D, Pendesini M, Coviello L, Jurman G, Furlanello C (2020) Precipitation nowcasting with orographic enhanced stacked generalization: improving deep learning predictions on extreme events. Atmosphere 11(3):267

    Article  Google Scholar 

  27. Hou S, Liu Y, Yang Q (2021) Real-time prediction of rock mass classification based on TBM operation big data and stacking technique of ensemble learning. J Rock Mech Geotech Eng 14(1):123–143

    Article  Google Scholar 

  28. Wolpert DH (1992) Stacked generalization Neural networks 5(2):241–259

    Article  Google Scholar 

  29. Kardani N, Zhou A, Nazem M, Shen SL (2020) Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. J Rock Mech Geotech Eng 13(3):188–201

    Google Scholar 

  30. Arslan H, Arslan H (2021) A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. Eng Sci Technol, an Int J 24(4):839–847

    Article  Google Scholar 

  31. Taneja S, Gupta C, Goyal K et al (2014) An enhanced k-nearest neighbor algorithm using information gain and clustering. In: Fourth International Conference on Advanced Computing & Communication Technologies. IEEE, pp 325–329. https://doi.org/10.1109/ACCT.2014.22

  32. Trstenjak B, Mikac S, Donko D (2014) KNN with TF-IDF based framework for text categorization. Procedia Eng 69:1356–1364

    Article  Google Scholar 

  33. Sivalenka V, Bai A (2021) An analysis on prediction of breast cancer using radius Nearest Neighbor algorithm over other classification algorithms[J]

  34. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 112. Springer

    Book  Google Scholar 

  35. Tripathi A, Goswami T, Trivedi SK, Sharma RD (2021) A multi class random forest (MCRF) model for classification of small plant peptides. Int J Inform Manag Data Insights 1(2):100029

    Google Scholar 

  36. Ruyu B, Mo H, Haifeng L (2019) A comparison of credit rating classification models based on spark-evidence from lending-club. Procedia Comput Sci 162:811–818

    Article  Google Scholar 

  37. Almugren N, Alshamlan HM (2019) New bio-marker gene discovery algorithms for cancer gene expression profile. IEEE Access 7:136907–136913

    Article  Google Scholar 

  38. Platt J (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classifiers 10(3):61–74

    Google Scholar 

  39. Singh KR, Neethu K, Madhurekaa K, Harita A, Mohan P (2021) Parallel SVM model for forest fire prediction. Soft Comput Lett 3:100014

    Article  Google Scholar 

  40. Martínez-Comesaña M, Ogando-Martínez A, Troncoso-Pastoriza F, López-Gómez J, Febrero-Garrido L, Granada-Álvarez E (2021) Use of optimised MLP neural networks for spatiotemporal estimation of indoor environmental conditions of existing buildings. Build Environ 205:108243

    Article  Google Scholar 

  41. Singh G, Sachan M (2015) Multi-layer perceptron (MLP) neural network technique for offline handwritten Gurmukhi character recognition. In: IEEE International Conference on Computational Intelligence & Computing Research, IEEE. https://doi.org/10.1109/ICCIC.2014.7238334

Download references

Funding

This work was supported by Tianshan Talent-Young Science and Technology Talent Project （NO.2022TSYCCX0060), Xinjiang Uygur Autonomous Region Youth Science Foundation Project (2022D01C695).

Author information

Author notes

  1. NaZiLa HaLiMaiMaiTi and Yue Hong contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. College of Software, Xinjiang University, Urumqi, 830046, China

    NaZiLa HaLiMaiMaiTi, Xiaoyi Lv & Cheng Chen

  2. People’s Hospital of Xinjiang Uygur Autonomous Region, UrumqiXinjiang, 830001, China

    Yue Hong

  3. College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Min Li & Chen Chen

  4. The Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi, 830011, China

    Hongtao Li

  5. The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, China

    Yunling Wang

  6. Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830046, China

    Xiaoyi Lv

  7. Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China

    Xiaoyi Lv

Authors
  1. NaZiLa HaLiMaiMaiTi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongtao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunling Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaoyi Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Cheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiaoyi Lv or Cheng Chen.

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

HaLiMaiMaiTi, N., Hong, Y., Li, M. et al. Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model. Med Biol Eng Comput 61, 3123–3135 (2023). https://doi.org/10.1007/s11517-023-02898-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02898-9

Keywords

Search

Navigation