[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Eyelid basal cell carcinoma classification using shallow and deep learning artificial neural networks

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

Results of exhaustive application of neural network pattern recognition and classification of cases suffering from eyelid basal cell carcinoma are reported. Recognition and classification were based on shallow and deep learning methods; namely, multi-layer error backpropagation and convolution neural networks were utilized. The processed material consisted of full-face, or half-face photographs of healthy subjects and patients suffering from eyelid basal cell carcinoma. Various training and learning methods were used and the efficiency of the proposed algorithms was evaluated using as performance metrics the accuracy score, that is, the ratio of the number of the correctly classified cases over the total number of cases under examination. With respect to the accuracy, some of the proposed algorithms reached up to 100%.

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
£29.99 /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 includes VAT (United Kingdom)

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

Explore related subjects

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

References

  • Allali J, D’Hermies F, Renard G (2005) Basal cell carcinomas of the eyelids. Ophthalmologica 219:57–71

    Article  Google Scholar 

  • Anderson NP, Anderson HE (1951) Development of basal cell epithelioma as a consequence of radiodermatitis. AMA Arch Derm Syphilol 63(5):586–596

    Article  Google Scholar 

  • Aquin G et al (2020) Novel nonlinear hypothesis for the delta parallel robot modeling. IEEE Access 8:46324–46334. https://doi.org/10.1109/ACCESS.2020.2979141

    Article  Google Scholar 

  • Chiang H-S, Chen M-Y, Huang Y-J (2019) Wavelet-based EEG processing for epilepsy detection using fuzzy entropy and associative petri net. IEEE Access 7:103255–103262. https://doi.org/10.1109/ACCESS.2019.2929266

    Article  Google Scholar 

  • Cosyantinescu R, Lazarescu V, Tahboub R (2008) Geometrical form recognition using “One Step Secant” algorithm in case of neural network. UPB Sci Bull Ser C 70(2):15–28

    Google Scholar 

  • De S, Mukherjee A, Ullah E (2018) Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration. arXiv2018, arXiv:1807.06766

  • Downes RN, Walker NP, Collin JR (1990) Micrographic (Mohs') surgery in the management of periocular basal cell epitheliomas. Eye (London) 5(1)160–168. https://doi.org/10.1038/eye.1990.21

  • de Rubio JJ (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309. https://doi.org/10.1109/TFUZZ.2009.2029569

    Article  Google Scholar 

  • de Rubio JJ (2020) Stability analysis of the modified Levenberg–Marquardt algorithm for the artificial neural network training. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3015200

    Article  Google Scholar 

  • Duong HQ, Copeland R (2001) Basal cell carcinoma, eyelid. eMedicine

  • Fletcher R, Powell MJD (1963) A rapidly convergent descent method for minimization. Br Comput J 163–168

  • Gaughan LJ, Bergeron JR, Mullins JF (1969) Giant basal cell epithelioma developing in acute burn site. Arch Dermatol 99(5):594–595

    Article  Google Scholar 

  • Gilbody JS, Aitken J, Green A (1994) What causes basal cell carcinoma to be the commonest cancer? Aust J Public Health 18:218–221

    Article  Google Scholar 

  • Gilde K (2006) Malignant tumors of the skin. Orv Hetil 147(48):2321–2330

    Google Scholar 

  • Goldberg DP (1997) Assessment and surgical treatment of basal cell skin cancer. Clin Plast Surg 24:673–686

    Article  Google Scholar 

  • Green A (1992) Changing patterns in incidence of non-melanoma skin cancer. Epithel Cell Biol 1:47–51

    Article  Google Scholar 

  • Hernandex G, Zamora E, Sossa H, Tellez G, Furlan F (2020) Hybrid neural networks for big data classification. Neurocomputing 390:327–340

    Article  Google Scholar 

  • Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980 or arXiv:1412.6980v9

  • Lindgren G, Larko O (1997) Long-term follow-up of cryosurgery of basal cell carcinoma of the eyelid. J Am Acad Dermatol 36:742–746

    Article  Google Scholar 

  • Livieris IE, Pintelas P (2012) An advanced conjugate gradient training algorithm based on a modified secant equation. Int Sch Res Not 2012: 486361. https://doi.org/10.5402/2012/486361

  • Mantzaris D, Anastassopoulos G, Adamopoulos A (2011) Genetic algorithm pruning of probabilistic neural networks in medical disease estimation. Neural Netw 24(8):831–835

    Article  Google Scholar 

  • Margolis MH (1970) Superficial multicentric basal cell epithelioma arising in thermal burn scar. Arch Dermatol 102(4):474–476

    Article  Google Scholar 

  • Matlab homepage. https://www.mathworks.com/products/matlab.html. Accessed 10 Dec 2020

  • Meda-Campana JA (2018) On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs. IEEE Access 6:31968–31973. https://doi.org/10.1109/ACCESS.2018.2846483

    Article  Google Scholar 

  • Miller SJ (1991) Biology of basal cell carcinoma (Part I). J Am Acad Dermatol 24:1–13

    Article  Google Scholar 

  • Polak E, Ribière G (1969) Note sur la convergence de methods de directions conjuguees. Revue Francais D’informatique Et De Recherche Operationnelle 16:35–43

    MATH  Google Scholar 

  • Prasad N, Singh R, Lal SP (2013) Comparison of back propagation and resilient propagation algorithm for spam classification. In: 2013 Fifth international conference on computational intelligence, modelling and simulation, Seoul, Korea (South), pp 29–34. https://doi.org/10.1109/CIMSim.2013.14

  • Preston DS, Stern RS (1992) Nonmelanoma cancers of the skin. N Eng J Med 327:1649–1662

    Article  Google Scholar 

  • Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12(1):145–151

    Article  Google Scholar 

  • Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE International Conference on Neural Networks 586–591. https://doi.org/10.1109/ICNN.1993.298623

  • Salomon J, Bieniek A, Baran E, Szepietowski JC (2004) Basal cell carcinoma on the eyelids: own experience. Dermatol Surg 30:257–263

    Google Scholar 

  • Saputra W, Tulus, Zarlis M, Sembiring RW, Hartama D (2017) Analysis resilient algorithm on artificial neural network backpropagation. J Phys: Conf Ser 930:012035. https://doi.org/10.1088/1742-6596/930/1/012035

    Article  Google Scholar 

  • Schulze HJ, Cribier B, Requena L (2005) Imiquimod 5% cream for the treatment of superficial basal cell carcinoma: results from a randomized vehicle-controlled phase III study in Europe. Br J Dermatol 152(5):939–947

    Article  Google Scholar 

  • Sharma N, Jain V, Mishra A (2018) An analysis of convolutional neural networks for image classification. Procedia Comput Sci 132:377–384. https://doi.org/10.1016/j.procs.2018.05.198

    Article  Google Scholar 

  • Solikhun, Wahyudi M, Safii M, Zarlis M (2020) Backpropagation network optimization using one step secant (OSS) algorithm. IOP Conf Ser: Mater Sci Eng 769:012037. https://doi.org/10.1088/1757-899X/769/1/012037

    Article  Google Scholar 

  • Stephanakis IM, Anastassopoulos GC (2013) A multiplicative multilinear model for inter-camera prediction in free view 3D systems. J Eng Intell Syst 21(2/3):193–207

    Google Scholar 

  • Stephanakis IM, Anastassopoulos GC, Iliadis L (2013) A self-organizing feature map (SOFM) model based on aggregate-ordering of local color vectors according to block similarity measures. Neurocomput J 107:97–107

    Article  Google Scholar 

  • Stephanakis IM, Iliou Th, Anastassopoulos G (2017) Information feature selection: using local attribute selections to represent connected distributions in complex datasets. In: Boracchi G, Iliadis L, Jayne C, Likas A (eds) Engineering applications of neural networks. EANN 2017, communications in computer and information science, vol 744. Springer, Cham, pp 441–450

    Google Scholar 

  • Stephanakis IM, Iliou Th, Anastassopoulos G (2018) Mutual information algorithms for optimal attribute selection in data driven partitions of databases. Evol Syst. https://doi.org/10.1007/s12530-018-9237-9

    Article  Google Scholar 

  • Sutskever I, Martens J, Dahl GE, Hinton GE (2013) On the importance of initialization and momentum in deep learning. ICML 28(3):1139–1147

    Google Scholar 

  • Telfer NR, Colver GB, Bowers PW (1999) Guidelines for the management of basal cell carcinoma. Br J Dermatol 141:415–423

    Article  Google Scholar 

  • Wilson AC, Roelofs R, Stern M, Srebro N, Recht B (2017) The marginal value of adaptive gradient methods in machine learning. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Advances in Neural Information Processing Systems 30, 31st Conference on neural information processing systems (NIPS 2017), Long Beach, CA, USA, pp. 4148–4158

  • Yaqub M, Feng J, Sultan Zia M, Arshid K, Jia K, Ur Rehman Z, Mehmood A (2020) State-of-the-art CNN optimizer for brain tumor segmentation in magnetic resonance images. Brain Sci 10:427. https://doi.org/10.3390/brainsci10070427

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Medical Physics, Department of Medicine, Democritus University of Thrace, Komotini, Greece

    Adam Adamopoulos & Evaggelos Georgios Chatzopoulos

  2. Laboratory of Informatics, Department of Medicine, Democritus University of Thrace, Komotini, Greece

    George Anastassopoulos

  3. Clinic of Ophthalmology, Department of Medicine, University of Crete, Heraklion, Greece

    Efstathios Detorakis

Authors
  1. Adam Adamopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Evaggelos Georgios Chatzopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. George Anastassopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Efstathios Detorakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adam Adamopoulos.

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

Adamopoulos, A., Chatzopoulos, E.G., Anastassopoulos, G. et al. Eyelid basal cell carcinoma classification using shallow and deep learning artificial neural networks. Evolving Systems 12, 583–590 (2021). https://doi.org/10.1007/s12530-021-09383-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-021-09383-4

Keywords

Search

Navigation