Abstract—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.
Index Terms—Deep learning, dermoscopy image, image processing, melanoma detection.
The authors are with De La Salle University, Philippines (e-mail: julie_salido@dlsu.edu.ph, conrado.ruiz@dlsu.edu.ph).
Cite: Julie Ann A. Salido and Conrado Ruiz Jr., "Using Deep Learning for Melanoma Detection in Dermoscopy Images," International Journal of Machine Learning and Computing vol. 8, no. 1, pp. 61-68, 2018.