Romero-Lopez A, Burdick J, Giró-i-Nieto X, Marques O. Skin Lesion Classification from Dermoscopic Images using Deep Learning. In The 13th IASTED International Conference on Biomedical Engineering (BioMed 2017). Innsbruck Austria; 2017.  (648.62 KB)


The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patient’s health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign.  The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66%, which is significantly higher than the current state of the art on that dataset.

[BioMed conference 2017]