Recent Deep Learning Methods for Melanoma Detection: A Review

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 834)

Abstract

Melanoma is a type of skin cancer, which is not that common like basal cell and squamous carcinoma, but it has dangerous implications since it has the tendency to migrate to other parts of body. So, if it is detected at an early stage then we can easily treat; otherwise it becomes fatal. Many computer-aided diagnostic methods using dermoscopy images have been proposed to assist the clinicians and dermatologists. Along with conventional methods which extract the low level handcrafted features, nowadays researchers have focused towards deep learning techniques which extract the deep and more generic features. Since 2012, deep learning has been applied to classification, segmentation, localization and many other fields and made an impact. This paper reviews about the deep learning techniques to detect melanoma cases from the rest skin lesion in clinical and dermoscopy images.

Keywords

Melanoma Dermoscope Deep learning Computer-assisted Diagnostics 

References

  1. 1.
    Satheesha, T., Satyanarayana, D., Prasad, M.G., Dhruve, K.D.: Melanoma is skin deep: a 3D reconstruction technique for computerized dermoscopic skin lesion classification. IEEE J. Trans. Eng. Health Med. 5, 1–17 (2017)CrossRefGoogle Scholar
  2. 2.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics. CA: A Cancer J. Clin. 67(1), 7–30 (2017)Google Scholar
  3. 3.
    Stolz, W., Riemann, A., Cognetta, A., Pillet, L., Abmayr, W., Holzel, D., Bilek, P., Nachbar, F., Landthaler, M.: ABCD rule of dermatoscopy: a new practical method for early recognition of malignant-melanoma. Eur. J. Dermatol. 4(7), 521–527 (1994)Google Scholar
  4. 4.
    Menzies, S.W., Ingvar, C., Crotty, K.A., McCarthy, W.H.: Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Arch. Dermatol. 132(10), 1178–1182 (1996)CrossRefGoogle Scholar
  5. 5.
    Sáez, A., Acha, B., Serrano, C.: Pattern analysis in dermoscopic images. In: Scharcanski, J., Celebi, M. (eds.) Computer Vision Techniques for the Diagnosis of Skin Cancer, pp. 23–48. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-39608-3_2CrossRefGoogle Scholar
  6. 6.
    Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Technol. Biomed. 13(5), 721–733 (2009)CrossRefGoogle Scholar
  7. 7.
    Argenziano, G., Fabbrocini, G., Carli, P., De Giorgi, V., Sammarco, E., Delfino, M.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions: comparison of the abcd rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134(12), 1563–1570 (1998)CrossRefGoogle Scholar
  8. 8.
    Gutman, D., Codella, N.C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., Halpern, A.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1605.01397 (2016)
  9. 9.
    Lee, T., Ng, V., Gallagher, R., Coldman, A., McLean, D.: Dullrazor®: a software approach to hair removal from images. Comput. Biol. Med. 27(6), 533–543 (1997)CrossRefGoogle Scholar
  10. 10.
    Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6), 362–373 (2007)CrossRefGoogle Scholar
  11. 11.
    Barata, C., Marques, J.S., Rozeira, J.: A system for the detection of pigment network in dermoscopy images using directional filters. IEEE Trans. Biomed. Eng. 59(10), 2744–2754 (2012)CrossRefGoogle Scholar
  12. 12.
    Silveira, M., Nascimento, J.C., Marques, J.S., Marçal, A.R., Mendonça, T., Yamauchi, S., Maeda, J., Rozeira, J.: Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J. Sel. Top. Sig. Process. 3(1), 35–45 (2009)CrossRefGoogle Scholar
  13. 13.
    Marques, J.S., Barata, C., Mendonça, T.: On the role of texture and color in the classification of dermoscopy images. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4402–4405. IEEE (2012)Google Scholar
  14. 14.
    Barata, C., Ruela, M., Francisco, M., Mendonça, T., Marques, J.S.: Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst. J. 8(3), 965–979 (2014)CrossRefGoogle Scholar
  15. 15.
    Situ, N., Wadhawan, T., Yuan, X., Zouridakis, G.: Modeling spatial relation in skin lesion images by the graph walk kernel. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6130–6133. IEEE (2010)Google Scholar
  16. 16.
    Barata, C., Figueiredo, M.A.T., Celebi, M.E., Marques, J.S.: Local features applied to dermoscopy images: bag-of-features versus sparse coding. In: Alexandre, L.A., Salvador Sánchez, J., Rodrigues, J.M.F. (eds.) IbPRIA 2017. LNCS, vol. 10255, pp. 528–536. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-58838-4_58CrossRefGoogle Scholar
  17. 17.
    Jafari, M.H., Samavi, S., Karimi, N., Soroushmehr, S.M.R., Ward, K., Najarian, K.: Automatic detection of melanoma using broad extraction of features from digital images. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 1357–1360. IEEE (2016)Google Scholar
  18. 18.
    Ballerini, L., Fisher, R.B., Aldridge, B., Rees, J.: A color and texture based hierarchical k-nn approach to the classification of non-melanoma skin lesions. In: Celebi, M., Schaefer, G. (eds.) Color Medical Image Analysis, pp. 63–86. Springer, Dordrecht (2013).  https://doi.org/10.1007/978-94-007-5389-1_4CrossRefGoogle Scholar
  19. 19.
    Blum, A., Luedtke, H., Ellwanger, U., Schwabe, R., Rassner, G., Garbe, C.: Digital image analysis for diagnosis of cutaneous melanoma. development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions. Br. J. Dermatol. 151(5), 1029–1038 (2004)CrossRefGoogle Scholar
  20. 20.
    Situ, N., Yuan, X., Chen, J., Zouridakis, G.: Malignant melanoma detection by bag-of-features classification. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2008, pp. 3110–3113. IEEE (2008)Google Scholar
  21. 21.
    Mishra, N.K., Celebi, M.E.: An overview of melanoma detection in dermoscopy images using image processing and machine learning. arXiv preprint arXiv:1601.07843 (2016)
  22. 22.
    Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69–90 (2012)CrossRefGoogle Scholar
  23. 23.
    Yu, Z., Ni, D., Chen, S., Qin, J., Li, S., Wang, T., Lei, B.: Hybrid dermoscopy image classification framework based on deep convolutional neural network and fisher vector. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 301–304. IEEE (2017)Google Scholar
  24. 24.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  25. 25.
    Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19, 221–248 (2017)CrossRefGoogle Scholar
  26. 26.
    Karpathy, A.: Cs231n: Convolutional neural networks for visual recognition. Neural Networks 1 (2016)Google Scholar
  27. 27.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)MATHGoogle Scholar
  28. 28.
    LeCun, Y., et al.: Lenet-5, convolutional neural networks (2015). http://yann.lecun.com/exdb/lenet
  29. 29.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10590-1_53CrossRefGoogle Scholar
  30. 30.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  31. 31.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  32. 32.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  33. 33.
    Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. arXiv preprint arXiv:1608.06993 (2016)
  34. 34.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Sabbaghi, S., Aldeen, M., Garnavi, R.: A deep bag-of-features model for the classification of melanomas in dermoscopy images. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 1369–1372. IEEE (2016)Google Scholar
  36. 36.
    Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp. 791–798. ACM (2007)Google Scholar
  37. 37.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. arXiv preprint arXiv:1703.02910 (2017)
  39. 39.
    Argenziano, G., Soyer, H., De Giorgi, V., Piccolo, D., Carli, P., Delfino, M., et al.: Dermoscopy: a tutorial. EDRA, Medical Publishing & New Media, p. 16 (2002)Google Scholar
  40. 40.
    Habif, M.T.: Dermnet skin diseases Atlas (1998)Google Scholar
  41. 41.
    Mendonça, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., Rozeira, J.: Ph 2-a dermoscopic image database for research and benchmarking. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), PP. 5437–5440. IEEE (2013)Google Scholar
  42. 42.
    Codella, N.C., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al.: Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1710.05006 (2017)
  43. 43.
    Giotis, I., Molders, N., Land, S., Biehl, M., Jonkman, M.F., Petkov, N.: Med-node: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. 42(19), 6578–6585 (2015)CrossRefGoogle Scholar
  44. 44.
    Ge, Z., Demyanov, S., Bozorgtabar, B., Abedini, M., Chakravorty, R., Bowling, A., Garnavi, R.: Exploiting local and generic features for accurate skin lesions classification using clinical and dermoscopy imaging. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), PP. 986–990. IEEE (2017)Google Scholar
  45. 45.
    Lopez, A.R., Giro-i Nieto, X., Burdick, J., Marques, O.: Skin lesion classification from dermoscopic images using deep learning techniques. In: 2017 13th IASTED International Conference on Biomedical Engineering (BioMed), pp. 49–54. IEEE (2017)Google Scholar
  46. 46.
    Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F.V., Avila, S., Valle, E.: Knowledge transfer for melanoma screening with deep learning. arXiv preprint arXiv:1703.07479 (2017)
  47. 47.
    Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)CrossRefGoogle Scholar
  48. 48.
    Kawahara, J., BenTaieb, A., Hamarneh, G.: Deep features to classify skin lesions. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1397–1400. IEEE (2016)Google Scholar
  49. 49.
    Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)
  50. 50.
    Demyanov, S., Chakravorty, R., Abedini, M., Halpern, A., Garnavi, R.: Classification of dermoscopy patterns using deep convolutional neural networks. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 364–368. IEEE (2016)Google Scholar
  51. 51.
    Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S.M.R., Jafari, M.H., Ward, K., Najarian, K.: Melanoma detection by analysis of clinical images using convolutional neural network. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 1373–1376. IEEE (2016)Google Scholar
  52. 52.
    Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36(4), 994–1004 (2017)CrossRefGoogle Scholar
  53. 53.
    Kawahara, J., Hamarneh, G.: Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.-I. (eds.) MLMI 2016. LNCS, vol. 10019, pp. 164–171. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-47157-0_20CrossRefGoogle Scholar
  54. 54.
    Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A., Smith, J.R.: Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds.) MLMI 2015. LNCS, vol. 9352, pp. 118–126. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24888-2_15CrossRefGoogle Scholar
  55. 55.
    Codella, N.C., Nguyen, Q.B., Pankanti, S., Gutman, D., Helba, B., Halpern, A., Smith, J.R.: Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4), 1–5 (2017)CrossRefGoogle Scholar
  56. 56.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electrical SciencesIIT BhubaneswarBhubaneswarIndia

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