Using deep learning for melanoma detection in dermoscopy images

Julie Ann A. Salido, Conrado Ruiz

Producció científica: Article en revista indexadaArticleAvaluat per experts

62 Cites (Scopus)


Melanoma is a common kind of cancer that affects a significant number of the population. Recently, deep learning techniques have achieved high accuracy rates in classifying images in various fields. This paper uses deep learning to automatically detect melanomas in dermoscopy images. The system first preprocesses the images by removing unwanted artifacts like hair removal and then automatically segments the skin lesion. It then classifies the images using Convolution Neural Network (CNN). The classifier has been tested on preprocessed and unprocessed dermoscopy images to evaluate its effectiveness. The results show an outstanding performance in terms of sensitivity, specificity and accuracy on the PH2 dataset. The system was able to achieve accuracies 93% for classifying melanoma and non-melanoma, with sensitivities and specificities in 86-94% range.

Idioma originalAnglès
Pàgines (de-a)61-68
Nombre de pàgines8
RevistaInternational Journal of Machine Learning and Computing
Estat de la publicacióPublicada - 1 de febr. 2018
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