Skin lesion classification from dermoscopic images using deep learning techniques
Document typeConference lecture
Rights accessOpen Access
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.
CitationRomero-Lopez, A., Giro, X., Burdick, J., Marques, O. Skin lesion classification from dermoscopic images using deep learning techniques. A: The IASTED International Conference on Biomedical Engineering. "Biomedical engineering 2017". Innsbruck: ACTA Press, 2017, p. 1-6.