Using deep learning for melanoma detection in dermoscopy images

• Main Authors: Salido, Julie Ann A., Ruiz, Conrado
• Format: text
• Published: Animo Repository 2018
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/806; https://animorepository.dlsu.edu.ph/context/faculty_research/article/1805/type/native/viewcontent
• Institution: De La Salle University

© 2018 International Association of Computer Science and Information Technology. 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.