Volume XLII-2/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 81-85, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-81-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 81-85, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-81-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 May 2019

09 May 2019

DEEP LEARNING BASED SKIN LESIONS DIAGNOSIS

D. A. Gavrilov, N. N. Shchelkunov, and A. V. Melerzanov D. A. Gavrilov et al.
  • Moscow Institute of Physics and Technology Dolgoprudny, Moscow region, Russia

Keywords: Deep convolutional neural networks, Melanoma diagnosis, Computer vision, Telemedicine

Abstract. Melanoma is one of the most virulent lesions of human’s skin. The visual diagnosis accuracy of melanoma directly depends on the doctor’s qualification and specialization. State-of-the-art solutions in the field of image processing and machine learning allows to create intelligent systems based on artificial convolutional neural network exceeding human’s rates in the field of object classification, including the case of malignant skin lesions. This paper presents an algorithm for the early melanoma diagnosis based on artificial deep convolutional neural networks. The algorithm proposed allows to reach the classification accuracy of melanoma at least 91%.