Skip to main content
Log in

Prediction and diagnosis of vertebral tumors on the Internet of Medical Things Platform using geometric rough propagation neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Vertebral tumors have a percentage of back pain that causes other vertebral region-born symptoms. Cancers that affect the vertebral column are visceral organ cancer metastases that are mostly seen in older patients. Vertebral dysfunction and neurological failure vertebral column cancers are the most important occurred cancers for patients. In the past, only few methods have been used to combat main and metastatic vertebral tumors. These methods are accessible for short-term monitoring and possess standardized classification consistency for vertebral diagnosis. In this paper, geometric rough propagation neural network has been used for the identification of genetic factors in the examination of a clinical sample with vertebral columns. The proposed neural network has C-statistics of 79.1%, a parameter pitch of 96.1%, and configuration for measurement in the study range with the Brier’s score of 95.6%. The algorithm shows great net gain on the decision curve study, with promising performance results of 98.5% on internal testing for preoperative non-routine estimation of discharges with 0.5% error rate and 96% accuracy range. Also, these models have been externally validated by the online healthcare careers cloud-based open access web application on Internet of Medical Things Platform with 97.9% specificity ratio.

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

Access this article

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.

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

Similar content being viewed by others

Explore related subjects

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

References

  1. Chandra SS, Xia Y, Engstrom C, Crozier S, Schwarz R, Fripp J (2014) Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med Image Anal 18(3):567–578

    Article  Google Scholar 

  2. Pereañez M (2017) Enlargement, subdivision and individualization of statistical shape models: application to 3D medical image segmentation (Doctoral dissertation, UniversitatPompeuFabra)

  3. Ma J, Wang A, Lin F, Wesarg S, Erdt M (2019) A novel robust kernel principal component analysis for nonlinear statistical shape modeling from erroneous data. Comput Med Imaging Gr 77:101638

    Article  Google Scholar 

  4. Liu N, Goodman SB, Lachiewicz PF, Wood KB (2019) Hip or spine surgery first? A survey of treatment order for patients with concurrent degenerative hip and spinal disorders. Bone Joint J 101(6_Supple_B):37–44

    Article  Google Scholar 

  5. Lu YC, Untaroiu CD (2013) Statistical shape analysis of clavicular cortical bone with applications to the development of mean and boundary shape models. Comput Methods Programs Biomed 111(3):613–628

    Article  Google Scholar 

  6. Shakeel PM, Burhanuddin MA, Desa MI (2019) Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks. Measurement. https://doi.org/10.1016/j.measurement.2019.05.027

    Article  Google Scholar 

  7. Wang J, Shi C (2017) Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy. Biomed Eng Online 16(1):49

    Article  Google Scholar 

  8. Kadoury S, Labelle H, Paragios N (2013) Spine segmentation in medical images using manifold embeddings and higher-order MRFs. IEEE Trans Med Imaging 32(7):1227–1238

    Article  Google Scholar 

  9. Shakeel PM, Tobely TE, Al-Feel H, Manogaran G, Baskar S (2019) Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access. https://doi.org/10.1109/access.2018.2883957

    Article  Google Scholar 

  10. Athertya JS, Kumar GS (2016) Automatic segmentation of vertebral contours from CT images using fuzzy corners. Comput Biol Med 72:75–89

    Article  Google Scholar 

  11. Melinska AU, Romaszkiewicz P, Wagel J, Antosik B, Sasiadek M, Iskander DR (2017) Statistical shape models of cuboid, navicular and talus bones. J Foot Ankle Res 10(1):6

    Article  Google Scholar 

  12. Yao J, Glocker B, Klinder T, Li S (eds) (2015) Recent advances in computational methods and clinical applications for spine imaging. Springer, Cham

    Google Scholar 

  13. Haq R, Aras R, Besachio DA, Borgie RC, Audette MA (2015) Minimally supervised segmentation and meshing of 3D intervertebral discs of the lumbar spine for discectomy simulation. In: Yao J, Glocker B, Klinder T, Li S (eds) Recent advances in computational methods and clinical applications for spine imaging. Springer, Cham, pp 143–155

    Chapter  Google Scholar 

  14. Lavecchia CE, Espino DM, Moerman KM, Tse KM, Robinson D, Lee PVS, Shepherd DET (2018) Lumbar model generator: a tool for the automated generation of a parametric scalable model of the lumbar spine. J R Soc Interface 15(138):20170829

    Article  Google Scholar 

  15. Zheng Q, Lu Z, Feng Q, Ma J, Yang W, Chen C, Chen W (2013) Adaptive segmentation of vertebral bodies from sagittal MR images based on local spatial information and Gaussian weighted Chi square distance. J Digit Imaging 26(3):578–593

    Article  Google Scholar 

  16. Yamaguchi S, Satake K, Yamaji Y, Chen YW, Tanaka HT (2014) Three-dimensional semiautomatic liver segmentation method for non-contrast computed tomography based on a correlation map of the locoregional histogram and probabilistic atlas. Comput Biol Med 55:79–85

    Article  Google Scholar 

  17. Elnakib A, Gimel’farb G, Suri JS, El-Baz A (2011) Medical image segmentation: a brief survey. In: Multi modality state-of-the-art medical image segmentation and registration methodologies. Springer, New York, pp 1–39

  18. Li H, Chen HC, Dolly S, Li H, Fischer-Valuck B, Victoria J et al (2016) An integrated model-driven method for in treatment upper airway motion tracking using cine MRI in head and neck radiation therapy. Med Phys 43(8Part1):4700–4710

    Article  Google Scholar 

  19. Sridhar KP, Baskar S, Shakeel PM, Dhulipala VS (2018) Developing brain abnormality recognize system using multi-objective pattern producing neural network. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-018-1058-y

    Article  Google Scholar 

  20. Lee S, Cho S, Ro YM (2011) Enhanced distal radius segmentation in DXA using modified ASM. IEICE TRANSACTIONS on Information and Systems 94(2):363–370

    Article  Google Scholar 

  21. Ravikumar N, Gooya A, Çimen S, Frangi AF, Taylor ZA (2018) Group-wise similarity registration of point sets using Student’s t-mixture model for statistical shape models. Med Image Anal 44:156–176

    Article  Google Scholar 

  22. Sarkalkan N, Weinans H, Zadpoor AA (2014) Statistical shape and appearance models of bones. Bone 60:129–140

    Article  Google Scholar 

  23. Lorenz C, Krahnstöver N (2000) Generation of point-based 3D statistical shape models for anatomical objects. Comput Vis Image Underst 77(2):175–191

    Article  Google Scholar 

  24. Haq R, Aras R, Besachio DA, Borgie RC, Audette MA (2015) 3D lumbar spine intervertebral disc segmentation and compression simulation from MRI using shape-aware models. Int J Comput Assist Radiol Surg 10(1):45–54

    Article  Google Scholar 

  25. Rasoulian A, Rohling R, Abolmaesumi P (2013) Lumbar spine segmentation using a statistical multi-vertebrae anatomical shape + pose model. IEEE Trans Med Imaging 32(10):1890–1900

    Article  Google Scholar 

  26. http://spineweb.digitalimaginggroup.ca/spineweb/index.php?n=Main.Datasets

  27. Leslie WD, Luo Y, Yang S, Goertzen AL, Ahmed S, Delubac I, Lix LM (2019) Fracture risk indices from DXA-based finite element analysis predict incident fractures independently from FRAX: the Manitoba BMD Registry. J Clin Densitom 22(3):338–345

    Article  Google Scholar 

  28. Massaad E, Fatima N, Hadzipasic M, Alvarez-Breckenridge C, Shankar GM, Shin JH (2019) Predictive analytics in spine oncology research: first steps, limitations, and future directions. Neurospine 16(4):669

    Article  Google Scholar 

  29. Urbaneja A, De Verbizier J, Formery AS, Tobon-Gomez C, Nace L, Blum A, Teixeira PAG (2019) Automatic rib cage unfolding with CT cylindrical projection reformat in polytraumatized patients for rib fracture detection and characterization: feasibility and clinical application. Eur J Radiol 110:121–127

    Article  Google Scholar 

  30. Yoganandan N, DeVogel N, Moore J, Pintar F, Banerjee A, Zhang J (2020) Human lumbar spine responses from vertical loading: ranking of forces via Brier score metrics and injury risk curves. Ann Biomed Eng 48(1):79–91

    Article  Google Scholar 

  31. Boschetti L, Flasse SP, Brivio PA (2004) Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: the Pareto Boundary. Remote Sens Environ 91(3–4):280–292

    Article  Google Scholar 

  32. Huang CWC, Ali A, Chang YM, Bezuidenhout AF, Ivanovic V, Rojas R, Bhadelia RA (2019) Performance of on-call radiology residents in interpreting total spine mri studies for the detection of spinal cord compression or cauda equina compression. Am J Roentgenol 213(6):1341–1347

    Article  Google Scholar 

  33. Dessouky M, Elrashidy M, Taha T, Abdelkader H (2015) Computer-aided diagnosis system for Alzheimer’s disease using different discrete transform techniques. Am J Alzheimer’s Dis Other Dementiasr 31(3):282–293

    Article  Google Scholar 

Download references

Acknowledgements

This work is funded by Researchers Supporting Project No. (RSP-2019/117), King Saud University, Riyadh, Saudi Arabia.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haytham Al-Feel.

Ethics declarations

Conflict of interest

The authors declare that they have no 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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fouad, H., Soliman, A.M., Hassanein, A.S. et al. Prediction and diagnosis of vertebral tumors on the Internet of Medical Things Platform using geometric rough propagation neural network. Neural Comput & Applic 34, 13133–13145 (2022). https://doi.org/10.1007/s00521-020-04935-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04935-2

Keywords