Skip to main content
SpringerLink
Log in

ABCD rule and pre-trained CNNs for melanoma diagnosis

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

Abstract

Skin cancer is the most common type of cancer and represents more than half of cancer diagnoses. Melanoma is the least frequent among skin cancers, but it is the most serious, with high potential for metastasis and can lead to death. However, melanoma is almost always curable if discovered in the early stages. In this context, computational methods for processing and analysis of skin lesion images have been studied and developed. This work proposes a computational approach to assist dermatologists in the diagnosis of skin lesions in melanoma or non-melanoma by means of dermoscopic images. The proposed methodology classifies skin lesions using a descriptor formed by the combination of the ABCD rule (Asymmetry, Border, Color, and Diameter) and pre-trained Convolutional Neural Networks (CNNs) features. The features were selected according to their gain ratios and used as input to the MultiLayer Perceptron classifier. We built a new database joining two distinct databases presented in the literature to validate the proposed methodology. The proposed method achieved an accuracy rate of 94.9% and Kappa index of 89.2%, which is considered “excellent”.

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

Similar content being viewed by others

References

  1. Al-Akaidi M (2004) Fractal speech processing. Cambridge University Press, Cambridge

    Book  Google Scholar 

  2. Argenziano G, Soyer H, De Giorgi V, Piccolo D, Carli P, Delfino M (2000) Interactive atlas of dermoscopy (book and cd-rom), http://www.dermoscopy.org/atlas/default.asp

  3. Barcelos CAZ, Boaventura M, Silva E (2003) A well-balanced flow equation for noise removal and edge detection. IEEE Trans Image Process 12(7):751–763

    Article  Google Scholar 

  4. Bhati P, Singhal M (2015) Early stage detection and classification of melanoma. In: Communication, control and intelligent systems (CCIS), 2015, pp 181–185. IEEE

  5. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  6. Campbell Jr JL (2012) Dermnet skin disease atlas, http://www.dermnet.com/

  7. Cavalcanti PG, Scharcanski J (2011) Automated prescreening of pigmented skin lesions using standard cameras. Comput Med Imaging Graph 35(6):481–491

    Article  Google Scholar 

  8. Cavalcanti PG, Scharcanski J, Baranoski GV (2013) A two-stage approach for discriminating melanocytic skin lesions using standard cameras. Expert Syst Appl 40(10):4054–4064

    Article  Google Scholar 

  9. Chang WY, Huang A, Yang CY, Lee CH, Chen YC, Wu TY, Chen GS (2013) Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study. PloS one 8(11):e76,212

    Article  Google Scholar 

  10. Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: Delving deep into convolutional nets. In: British machine vision conference

  11. Chimieski BF, Fagundes RDR (2013) Association and classification data mining algorithms comparison over medical datasets. Journal of health informatics 5(2):44–51

    Google Scholar 

  12. Chu A, Sehgal CM, Greenleaf JF (1990) Use of gray value distribution of run lengths for texture analysis. Pattern Recogn Lett 11(6):415–419

    Article  Google Scholar 

  13. Codella N, Cai J, Abedini M, Garnavi R, Halpern A, Smith JR (2015) Deep learning, sparse coding, and svm for melanoma recognition in dermoscopy images. In: International workshop on machine learning in medical imaging, pp 118–126. Springer, Berlin

    Chapter  Google Scholar 

  14. Cohen BA, Lehmann CU (2012) Johns hopkins university - dermatlas, dermatology image atlas .http://dermatlas.med.jhmi.edu/derm

  15. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  16. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Computer vision and pattern recognition, 2005. CVPR 2005. IEEE computer society conference on, vol 1, pp 886–893.IEEE

  17. Dasarathyand BR, Holder EB (1991) Image characterizations based on joint gray-level run-length distributions. Pattern Recogn Lett 12(8):497–502

    Article  Google Scholar 

  18. Diepgen TL, Yihune G (2016) Dermatology information system – dermis. http://dermis.net/, (2012). Accessed September 12

  19. Fix E, Hodges Jr JL (1951) Discriminatory analysis-nonparametric discrimination: consistency properties, Tech. rep., California Univ Berkeley

  20. Galloway MM (1975) Texture analysis using gray level run lengths. Computer graphics and image processing 4(2):172–179

    Article  Google Scholar 

  21. Guide SC (2012) Melanoma. http://www.skincancerguide.ca/melanoma/images/melanoma_images.html

  22. Gutiérrez PA, Hervás-martínez C, Martínez-Estudillo FJ (2011) Logistic regression by means of evolutionary radial basis function neural networks. IEEE Transactions on Neural Networks 22(2):246–263

    Article  Google Scholar 

  23. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD explorations newsletter 11(1):10–18

    Article  Google Scholar 

  24. Halpern A, Marghoob A, Zemtsov A, Scope A, Kheterpal M (2015) International skin imaging collaboration. https://isic-archive.com/

  25. Haralick RM, Shanmugam K, et al. (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  26. Haykin SS (2009) Neural networks and learning machines, vol 3. Pearson Upper Saddle River, NJ, USA

  27. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22Nd ACM International Conference on Multimedia, MM ’14. New York, USA, pp 675–678

  28. Kasmi R, Mokrani K (2016) Classification of malignant melanoma and benign skin lesions: implementation of automatic abcd rule. IET Image Process 10(6):448–455

    Article  Google Scholar 

  29. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25. Curran Associates Inc, pp 1097-1105

  30. Kumar A, Kim J, Lyndon D, Fulham M, Feng D (2016) An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE Journal of Biomedical and Health Informatics 21:31–40

    Article  Google Scholar 

  31. Lacy K, Alwan W (2013) Skin cancer. Med 41(7):402–405

    Article  Google Scholar 

  32. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    Article  Google Scholar 

  33. Li SZ (2009) Markov random field modeling in image analysis Springer Science & Business Media

  34. Melton JL, Swanson JR (2012) Loyola university dermatology medical education, skin cancer and benign tumor image atlas. http://www.meddean.luc.edu/lumen/MedEd/medicine/dermatology/melton/content1.html

  35. Mendonça T, Ferreira PM, Marques JS, Marcal AR, Rozeira J (2013) Ph2-a dermoscopic image database for research and benchmarking. In: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp 5437–5440. IEEE. https://www.fc.up.pt/addi/ph2

  36. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59

    Article  Google Scholar 

  37. Oliveira RB, Marranghello N, Pereira AS, Tavares JMR (2016) A computational approach for detecting pigmented skin lesions in macroscopic images. Expert Syst Appl 61:53–63

    Article  Google Scholar 

  38. Powers D (2007) Evaluation: from precision, recall and f-factor to roc, informedness, markedness and correlation. Adelaide, Australia

    Google Scholar 

  39. Rosenfield GH, Fitzpatrick-Lins K (1986) A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric engineering and remote sensing 52(2):223–227

    Google Scholar 

  40. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Tech. rep. California Univ San Diego La Jolla Inst for Cognitive Science

  41. Sánchez-Monedero J, Sáez A, Pérez-Ortiz M, Gutiérrez PA, Hervás-martínez C (2016) Classification of melanoma presence and thickness based on computational image analysis. In: International conference on hybrid artificial intelligence systems, pp 427–438. Springer, Berlin

    Google Scholar 

  42. SCF (2017) Skin cancer & facts statistics. http://www.skincancer.org/skin-cancer-information/skin-cancer-facts, Accessed May 15

  43. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  44. Sobel I (1970) Camera models and machine perception. Tech. rep., DTIC Document

  45. Society AC (2018) Cancer facts and figures 2018. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2018/cancer-facts-and-figures-2018.pdf. Accessed May 3, 2018

  46. Suzumura Y (2012) Ysp dermatology image database. http://homepage1.nifty.com/ysh/soft_e_ysp.htm

  47. Tajbakhsz N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans Med Imaging 35:1299–1312

    Article  Google Scholar 

  48. Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Transactions on Systems Man, and Cybernetics 8(6):460–473

    Article  Google Scholar 

  49. Total S (2012) Câncer da pele: fotoproteção, vida saudável com o sol. http://www.saudetotal.com.br/prevencao/topicos/default.asp

  50. UK CR (2017) Skin cancer. http://www.cancerresearchuk.org/about-cancer/skin-cancer. Accessed March 15, 2018

  51. Vogado LH, Veras RM, Araujo FH, Silva RR, Aires KR (2018) Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Eng Appl Artif Intell 72:415–422. https://doi.org/10.1016/j.engappai.2018.04.024. https://www.sciencedirect.com/science/article/pii/S0952197618301039

    Article  Google Scholar 

  52. Wang SH, Sun J, Phillips P, Zhao G, Zhang YD (2017) Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units. Journal of Real-Time Image Processing, pp 1–12. https://doi.org/10.1007/s11554-017-0717-0.

    Article  Google Scholar 

  53. WHO How common is skin cancer? http://www.who.int/uv/faq/skincancer/en/index1.html. Accessed May 15, 2017

  54. Zhang YD, Dong Z, Chen X, Jia W, Du S, Muhammad K, Wang SH (2017) Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-017-5243-3.

    Article  Google Scholar 

  55. Zhang Y.D, Muhammad K, Tang C (2018) Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on gpu platform. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-018-5765-3.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Federal University of Piauí, Teresina, PI, Brazil

    Nayara Moura, Rodrigo Veras, Kelson Aires, Vinícius Machado & Maíla Claro

  2. Federal University of Piauí, Picos, PI, Brazil

    Romuere Silva & Flávio Araújo

Authors
  1. Nayara Moura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rodrigo Veras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kelson Aires
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vinícius Machado
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Romuere Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Flávio Araújo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maíla Claro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nayara Moura.

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

Moura, N., Veras, R., Aires, K. et al. ABCD rule and pre-trained CNNs for melanoma diagnosis. Multimed Tools Appl 78, 6869–6888 (2019). https://doi.org/10.1007/s11042-018-6404-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6404-8

Keywords

Search

Navigation