C. Gaudy-marqueste, Ugly duckling sign as a major factor of efficiency in melanoma detection, JAMA dermatology, vol.153, issue.4, pp.279-284, 2017.

A. Esteva, Dermatologist-level classification of skin cancer with deep neural networks, Nature, vol.542, issue.7639, p.115, 2017.

, Collection Les Données, p.254, 2017.

C. Longvert and P. Saiag, Actualités dans le mélanome cutané. La revue de médecine interne, vol.40, pp.178-183, 2019.

W. Stolz, A. Riemann, and A. B. Cognetta, ABCD rule of dermatoscopy: A new practical method for early recognition of malignant melanoma, European journal of dermatology, vol.4, issue.7, pp.521-527, 1994.

G. Capdehourat, Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions. Pattern recognition letters, vol.32, pp.2187-2196, 2011.

H. Lorentzen, K. Weismann, and L. Secher, The dermatoscopic ABCD rule does not improve diagnostic accuracy of malignant melanoma, Acta Dermato-venereologica, vol.79, issue.6, pp.469-472, 1999.

R. Johr, Dermoscopy: alternative melanocytic algorithms-the ABCD rule of dermatoscopy, menzies scoring method, and 7-point checklist, Clinics in dermatology, vol.20, issue.3, pp.240-247, 2002.

T. Lee, Dullrazor®: A software approach to hair removal from images. Computers in biology and medicine, vol.27, pp.533-543, 1997.

M. E. Celebi, A methodological approach to the classification of dermoscopy images. Computerized Medical imaging and graphics, vol.31, pp.362-373, 2007.

C. Barata, J. S. Marques, and J. Rozeira, A system for the detection of pigment network in dermoscopy images using directional filters, IEEE transactions on biomedical engineering, vol.59, issue.10, pp.2744-2754, 2012.

J. S. Marques, C. Barata, and T. Mendonça, On the role of texture and color in the classification of dermoscopy images, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012.

D. Bisla, Skin Lesion Segmentation and Classification with Deep Learning System, 2019.

O. Ronneberger, . Fischer, . Philipp, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, pp.234-241, 2015.

H. Sood and M. Shukla, Various Techniques for Detecting Skin Lesion: A Review, International Journal of Computer Science and Mobile Computing, vol.3, issue.5, pp.905-912, 2014.

M. Silveira, Comparison of segmentation methods for melanoma diagnosis in dermoscopy images, IEEE Journal of Selected Topics in Signal Processing, vol.3, issue.1, pp.35-45, 2009.

A. A. Adeyinka and S. Viriri, Skin Lesion Images Segmentation: A Survey of the State-of-the-Art, International Conference on Mining Intelligence and Knowledge Exploration, 2018.

W. Chang, The feasibility of using manual segmentation in a multifeature computeraided diagnosis system for classification of skin lesions: a retrospective comparative study, BMJ open, vol.5, issue.4, p.7823, 2015.

M. Nasir, An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microscopy research and technique, vol.81, pp.528-543, 2018.

R. B. Oliveira, Computational methods for pigmented skin lesion classification in images: review and future trends, Neural Computing and Applications, vol.29, issue.3, pp.613-636, 2018.

D. A. Okuboyejo and O. O. Olugbara, A review of prevalent methods for automatic skin lesion diagnosis, The Open Dermatology Journal, vol.12, issue.1, 2018.

N. K. Mishra and M. E. Celebi, An overview of melanoma detection in dermoscopy images using image processing and machine learning, 2016.

I. Maglogiannis and C. N. Doukas, Overview of advanced computer vision systems for skin lesions characterization, IEEE transactions on information technology in biomedicine, vol.13, pp.721-733, 2009.

S. Pathan, K. G. Prabhu, and P. Siddalingaswamy, Techniques and algorithms for computer aided diagnosis of pigmented skin lesions-A review, Biomedical Signal Processing and Control, vol.39, pp.237-262, 2018.

T. J. Brinker, Skin cancer classification using convolutional neural networks: systematic review, Journal of medical Internet research, vol.20, issue.10, p.11936, 2018.

N. N. Sultana and N. Puhan, Recent Deep Learning Methods for Melanoma Detection: A Review, International Conference on Mathematics and Computing, 2018.

A. Mahbod, Skin lesion classification using hybrid deep neural networks, ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks. in Advances in neural information processing systems, 2012.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

K. He, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.

C. Szegedy, Rethinking the inception architecture for computer vision, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.

A. R. Lopez, Skin lesion classification from dermoscopic images using deep learning techniques, 2017 13th IASTED International Conference on Biomedical Engineering, 2017.

J. Kawahara and G. Hamarneh, Multi-resolution-tract CNN with hybrid pretrained and skinlesion trained layers, International Workshop on Machine Learning in Medical Imaging, 2016.

J. Kawahara, A. Bentaieb, and G. Hamarneh, Deep features to classify skin lesions, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016.

X. Zhang, Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge, BMC Medical Informatics and Decision Making, vol.18, issue.2, p.59, 2018.

A. Radford, L. Metz, and S. Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015.

C. Barata, M. E. Celebi, and J. S. Marques, Improving dermoscopy image classification using color constancy, IEEE journal of biomedical and health informatics, vol.19, issue.3, pp.1146-1152, 2015.

B. Zhou, Learning deep features for discriminative localization, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.

X. Jia and L. Shen, Skin lesion classification using class activation map, 2017.

J. Hagerty, Deep Learning and Handcrafted Method Fusion: Higher Diagnostic Accuracy for Melanoma Dermoscopy Images, IEEE Journal of Biomedical and Health Informatics, pp.1-1, 2019.

A. N. Recherche, SKIN ANalyzer : diagnostic du mélanome assisté par ordinateur -Skinan, 2019.

A. Medical and . Technologie, 06 mai, 2019.

Y. Wazaefi, S. Paris, and B. Fertil, Contribution of a classifier of skin lesions to the dermatologist's decision, 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), 2012.

H. Ganster, Automated melanoma recognition, IEEE transactions on medical imaging, vol.20, issue.3, pp.233-239, 2001.

R. Fan, LIBLINEAR: A library for large linear classification, Journal of machine learning research, vol.9, pp.1871-1874, 2008.

F. Pedregosa, Scikit-learn: Machine learning in Python, Journal of machine learning research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

F. Chollet and . Keras, , 2015.

J. Sanders and E. Kandrot, CUDA by example: an introduction to general-purpose GPU programming, 2010.