Skip to main content
SpringerLink
Log in

An Improved VGG Model for Skin Cancer Detection

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Skin cancer is one of the most prevalent malignancies in humans and is generally diagnosed through visual means. Since it is essential to detect this type of cancer in its early phases, one of the challenging tasks in developing and designing digital medical systems is the development of an automated classification system for skin lesions. For the automated detection of melanoma, a serious form of skin cancer, using dermoscopic images, convolutional neural network (CNN) models are getting noticed more than ever before. This study presents a new model for the early detection of skin cancer on the basis of processing dermoscopic images. The model works based on a well-known CNN-based architecture called the VGG-16 network. The proposed framework employs an enhanced architecture of VGG-16 to develop a model, which contributes to the improvement of accuracy in skin cancer detection. To evaluate the proposed technique, we have conducted a comparative study between our method and a number of previously introduced techniques on the International Skin Image Collaboration dataset. According to the results, the proposed model outperforms the compared alternative techniques in terms of accuracy.

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
Fig. 7

Similar content being viewed by others

Abbreviations

AUC:

Receiver operating characteristic curve

BN:

Batch normalization

CNN:

Convolutional neural network

DCNN:

Deep convolutional neural network

ECOC:

Error-correcting output codes

GAP:

Global average pooling

GWO:

Grey wolf optimization

ISIC:

International skin image collaboration

IWOA:

Improved whale optimization algorithm

KNN:

K-nearest neighbour

OCNN:

Optimized convolutional neural networks

PSO:

Particle swarm optimization

ROC:

Receiver operating characteristic

SVM:

Support vector machine

References

  1. Dascalu A, David EO (2019) Skin cancer detection by deep learning and sound analysis algorithms: a prospective clinical study of an elementary dermoscope. EBioMedicine 43:107–113. https://doi.org/10.1016/j.ebiom.2019.04.055

    Article  Google Scholar 

  2. Petrie T, Samatham R, Witkowski AM, Esteva A, Leachman SA (2019) Melanoma early detection: big data, bigger picture. J Investig Dermatol 139(1):25–30. https://doi.org/10.1016/j.jid.2018.06.187

    Article  Google Scholar 

  3. Marks R (2000) Epidemiology of melanoma. Clin Exp Dermatol 25(6):459–463. https://doi.org/10.1046/j.1365-2230.2000.00693.x

    Article  Google Scholar 

  4. Arora G, Dubey AK, Jaffery ZA, Rocha A (2020) Bag of feature and support vector machine based early diagnosis of skin cancer. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05212-y

    Article  Google Scholar 

  5. Kaur S et al (2020) Medical diagnostic systems using artificial intelligence (AI) algorithms: principles and perspectives. IEEE Access 8:228049–228069. https://doi.org/10.1109/access.2020.3042273

    Article  Google Scholar 

  6. Salehi M, Razmara J, Lotfi S (2020) A novel data mining on breast cancer survivability using MLP ensemble learners. Comput J 63(3):435–447

    Article  Google Scholar 

  7. Razmara J, Zaboli MH, Hassankhani H (2016) Elderly fall risk prediction based on a physiological profile approach using artificial neural networks. Health Inform J 24(4):410–418

    Article  Google Scholar 

  8. Dwivedi YK et al (2021) Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manag 57:101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

    Article  Google Scholar 

  9. Salehi M, Razmara J, Lotfi S, Mahan F (2021) A one-dimensional probabilistic convolutional neural network for prediction of breast cancer survivability. Comput J. https://doi.org/10.1093/comjnl/bxab096

    Article  Google Scholar 

  10. Zaidan AA et al (2018) A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Health Technol 8(4):223–238. https://doi.org/10.1007/s12553-018-0223-9

    Article  Google Scholar 

  11. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11):e00938. https://doi.org/10.1016/j.heliyon.2018.e00938

    Article  Google Scholar 

  12. Esteva A et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118. https://doi.org/10.1038/nature21056

    Article  Google Scholar 

  13. Yu L, Chen H, Dou Q, Qin J, Heng P-A (2017) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imag 36(4):994–1004. https://doi.org/10.1109/tmi.2016.2642839

    Article  Google Scholar 

  14. Haenssle HA et al (2018) Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29(8):1836–1842. https://doi.org/10.1093/annonc/mdy166

    Article  Google Scholar 

  15. Gola Isasi A, García Zapirain B, Méndez Zorrilla A (2011) Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms. Comput Biol Med 41(9):742–755. https://doi.org/10.1016/j.compbiomed.2011.06.010

    Article  Google Scholar 

  16. Ramlakhan K, Shang Y (2011) A mobile automated skin lesion classification system. In: 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence. IEEE. https://doi.org/10.1109/ictai.2011.29.

  17. Zhang N, Cai Y-X, Wang Y-Y, Tian Y-T, Wang X-L, Badami B (2020) Skin cancer diagnosis based on optimized convolutional neural network. Artif Intell Med 102:101756. https://doi.org/10.1016/j.artmed.2019.101756

    Article  Google Scholar 

  18. Tan TY, Zhang L, Neoh SC, Lim CP (2018) Intelligent skin cancer detection using enhanced particle swarm optimization. Knowl Based Syst 158:118–135. https://doi.org/10.1016/j.knosys.2018.05.042

    Article  Google Scholar 

  19. Mohakud R, Dash R (2021) “Designing a grey wolf optimization based hyper-parameter optimized convolutional neural network classifier for skin cancer detection. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.05.012

    Article  Google Scholar 

  20. Dorj U-O, Lee K-K, Choi J-Y, Lee M (2018) The skin cancer classification using deep convolutional neural network. Multimed Tools Appl 77(8):9909–9924. https://doi.org/10.1007/s11042-018-5714-1

    Article  Google Scholar 

  21. Ali MS, Miah MS, Haque J, Rahman MM, Islam MK (2021) An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Mach Learn Appl 5:100036. https://doi.org/10.1016/j.mlwa.2021.100036

    Article  Google Scholar 

  22. Dargan S, Kumar M, Ayyagari MR, Kumar G (2019) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 27(4):1071–1092. https://doi.org/10.1007/s11831-019-09344-w

    Article  MathSciNet  Google Scholar 

  23. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26. https://doi.org/10.1016/j.neucom.2016.12.038

    Article  Google Scholar 

  24. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition," arXiv preprint, http://arXiv.org/abs/1409.1556

  25. International Skin Imaging Collaboration “SIIM-ISIC (2020) Challenge Dataset”. Int Skin Imag Collab. https://doi.org/10.34970/2020-DS01

    Article  Google Scholar 

  26. Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5(2):01–11. https://doi.org/10.5121/ijdkp.2015.5201

    Article  Google Scholar 

  27. Jiménez-Valverde A (2011) Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecol Biogeogr 21(4):498–507. https://doi.org/10.1111/j.1466-8238.2011.00683.x

    Article  Google Scholar 

  28. Kuo C-CJ (2016) Understanding convolutional neural networks with a mathematical model. J Vis Commun Image Represent 41:406–413. https://doi.org/10.1016/j.jvcir.2016.11.003

    Article  Google Scholar 

  29. Wu Z, Shen C, van den Hengel A (2019) Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognit 90:119–133. https://doi.org/10.1016/j.patcog.2019.01.006

    Article  Google Scholar 

  30. Krizhevsky A, Sutskever I, Hinton G (2013) Imagenet classification with deep convolutional neural networks, Proc 25th Int Conf Neural Inf Process Syst, pp 1097–1105

  31. Wilcoxon F (1992) Individual comparisons by ranking methods. In: Kotz S, Johnson NL (eds) Springer series in statistics. Springer, New York, pp 196–202. https://doi.org/10.1007/978-1-4612-4380-9_16

    Chapter  Google Scholar 

Download references

Acknowledgements

The authors would like to express their thanks to the editors and the anonymous referees for their insightful comments and suggestions that greatly improved the paper. The authors like to acknowledge the Research Center for Pharmaceutical Nanotechnology at Tabriz University of Medical Sciences (#69144).

Author information

Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematics, Statistics, and Computer Science, University of Tabriz, Tabriz, Iran

    Hamed Tabrizchi & Jafar Razmara

  2. Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran

    Sepideh Parvizpour

  3. Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

    Sepideh Parvizpour

Authors
  1. Hamed Tabrizchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sepideh Parvizpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jafar Razmara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jafar Razmara.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This paper does not contain any studies with human participants or animals performed by any of the authors.

Human and Animal Rights

This paper does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

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

Tabrizchi, H., Parvizpour, S. & Razmara, J. An Improved VGG Model for Skin Cancer Detection. Neural Process Lett 55, 3715–3732 (2023). https://doi.org/10.1007/s11063-022-10927-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-022-10927-1

Keywords

Search

Navigation