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Use of Neural Network-Based Deep Learning Techniques for the Diagnostics of Skin Diseases

Biomedical Engineering volume 52, pages 348–352 (2019)Cite this article

Melanoma is one of the most dangerous types of cancer. The accuracy of visual diagnosis of melanoma directly depends on the experience and specialty of the physician. Current development of image processing and machine learning technologies allows systems based on artificial neural convolutional networks to be created, these being better than humans in object classification tasks, including the diagnostics of malignant skin neoplasms. Presented here is an algorithm for the early diagnostics of melanoma based on artificial deep convolutional neural networks. This algorithm can discriminate benign and malignant skin tumors with an accuracy of at least 91% by examination of dermatoscopy images.

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Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia

    D. A. Gavrilov, A. V. Melerzanov, N. N. Shchelkunov & E. I. Zakirov

Authors
  1. D. A. Gavrilov
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  2. A. V. Melerzanov
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  3. N. N. Shchelkunov
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  4. E. I. Zakirov
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Correspondence to D. A. Gavrilov.

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Translated from Meditsinskaya Tekhnika, Vol. 52, No. 5, Sep.-Oct., 2018, pp. 40-44.

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Gavrilov, D.A., Melerzanov, A.V., Shchelkunov, N.N. et al. Use of Neural Network-Based Deep Learning Techniques for the Diagnostics of Skin Diseases. Biomed Eng 52, 348–352 (2019). https://doi.org/10.1007/s10527-019-09845-9

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  • DOI: https://doi.org/10.1007/s10527-019-09845-9