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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(1): 61-68 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.1.664

Using Deep Learning for Melanoma Detection in Dermoscopy Images

Julie Ann A. Salido and Conrado Ruiz Jr.
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.

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