Benign and Malignant Skin Lesion Classification Comparison for Three Deep-Learning Architectures

  • Ercument YilmazEmail author
  • Maria Trocan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)


Early detection of melanoma, which is a deadly form of skin cancer, is vital for patients. Differential diagnosis of malignant and benign melanoma is a challenging task even for specialist dermatologists. The diagnostic performance of melanoma has significantly improved with the use of images obtained via dermoscopy devices. With the recent advances in medical image processing field, it is possible to improve the dermatological diagnostic performance by using computer-assisted diagnostic systems. For this purpose, various machine learning algorithms are designed and tested to be used in the diagnosis of melanoma. Deep learning models, which have gained popularity in recent years, have been effective in solving image recognition and classification problems. Concurrently with these developments, studies on the classification of dermoscopic images using CNN models are being performed. In this study, the performance of AlexNet, GoogLeNet and Resnet50 CNNs were examined for the classification problem of benign and malignant melanoma cancers on dermoscopic images. Dermoscopic images of 19373 benign and 2197 malignant lesions obtained from ISIC database were used in the experiments. All three CNNs, which were the former winners of the ImageNet competition, have been reconfigured to perform binary classification. In the experiments 80% of the images were used for training and the remaining 20% were used for validation. All experiments were performed with the same parameters for each CNN models. According to the experiments ResNET50 model achieved the best performance with 92.81% classification accuracy and AlexNet was first-ranked in terms of the time complexity measurements. The development of new models based on existing CNN models with a focus on dermoscopic images will be the subject of future studies.


Melanoma Benign and Malignant lesion classification Deep learning Convolutional neural networks AlexNet GoogLeNet ResNet 


  1. 1.
    Miller, A.J., Mihm, M.C.: Melanoma. N. Engl. J. Med. 355(1), 51–65 (2006)CrossRefGoogle Scholar
  2. 2.
    Argenziano, G., et al.: Accuracy in melanoma detection: a 10-year multicenter survey. J. Am. Acad. Dermatol. 67(1), 54–59.e1 (2012)CrossRefGoogle Scholar
  3. 3.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA Cancer J. Clin. 69(1), 7–34 (2019)CrossRefGoogle Scholar
  4. 4.
    Rigel, D.S., Russak, J., Friedman, R.: The evolution of melanoma diagnosis: 25 years beyond the ABCDs. CA Cancer J. Clin. 60(5), 301–316 (2010)CrossRefGoogle Scholar
  5. 5.
    Darragh, C.T., Clayton, A.S.: Melanoma in situ. In: Hanlon, A. (ed.) A Practical Guide to Skin Cancer, pp. 97–115. Springer, Cham (2018). Scholar
  6. 6.
    Errichetti, E., Stinco, G.: Dermoscopy in general dermatology: a practical overview. Dermatol. Ther. 6(4), 471–507 (2016)CrossRefGoogle Scholar
  7. 7.
    Kittler, H., Pehamberger, H., Wolff, K., Binder, M.: Diagnostic accuracy of dermoscopy. Lancet Oncol. 3(3), 159–165 (2002)CrossRefGoogle Scholar
  8. 8.
    Sinz, C., et al.: Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin. J. Am. Acad. Dermatol. 77(6), 1100–1109 (2017)CrossRefGoogle Scholar
  9. 9.
    Winterbottom, A., Harcourt, D.: Patients’ experience of the diagnosis and treatment of skin cancer. J. Adv. Nurs. 48(3), 226–233 (2004)CrossRefGoogle Scholar
  10. 10.
    Lee, J.J., English, J.C.: Teledermatology: a review and update. Am. J. Clin. Dermatol. 19(2), 253–260 (2018)CrossRefGoogle Scholar
  11. 11.
    Marghoob, A.A., Scope, A.: The complexity of diagnosing melanoma. J. Invest. Dermatol. 129(1), 11–13 (2009)CrossRefGoogle Scholar
  12. 12.
    Finnane, A., Dallest, K., Janda, M., Soyer, H.P.: Teledermatology for the diagnosis and management of skin cancer: a systematic review. JAMA Dermatol. 153(3), 319–327 (2017)CrossRefGoogle Scholar
  13. 13.
    Petrie, T., Samatham, R., Witkowski, A.M., Esteva, A., Leachman, S.A.: Melanoma early detection: big data, bigger picture. J. Invest. Dermatol. 139(1), 25–30 (2019)CrossRefGoogle Scholar
  14. 14.
    Liu, X., et al.: A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1(6), e271–e297 (2019)CrossRefGoogle Scholar
  15. 15.
    Haenssle, H.A., et al.: 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 (2018)CrossRefGoogle Scholar
  16. 16.
    Brinker, T.J., et al.: Deep neural networks are superior to dermatologists in melanoma image classification. Eur. J. Cancer 119, 11–17 (2019)CrossRefGoogle Scholar
  17. 17.
    Hekler, A., et al.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur. J. Cancer 118, 91–96 (2019)CrossRefGoogle Scholar
  18. 18.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  19. 19.
    Krizhevsky, A., Sutskever, I., Hinton, G: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
  20. 20.
    Szegedy, C., et al.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, pp. 1–9 (2015)Google Scholar
  21. 21.
    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, Las Vegas, pp. 770–778 (2016)Google Scholar
  22. 22.
    International Skin Imaging Collaboration (ISIC) Project. Accessed 1 Oct 2019
  23. 23.
    Suzuki, N.M., Saraiva, M.I.R., Capareli, G.C., Castro, L.G.M.: Histologic review of melanomas by pathologists trained in melanocytic lesions may change therapeutic approach in up to 41.9% of cases. Anais Bras. Dermatol. 93(5), 752–754 (2018)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Karadeniz Technical UniversityTrabzonTurkey
  2. 2.Institut Supérieur d’Électronique de ParisParisFrance

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