Abstract
In this work, we report the use of convolutional neural networks for the detection of malignant melanomas against nevus skin lesions in a dataset of dermoscopic images of the same magnification. The technique of transfer learning is utilized to compensate for the limited size of the available image dataset. Results show that including transfer learning in training CNN architectures improves significantly the achieved classification results.
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We gratefully acknowledge the support of NVDIA Corporation for the donation of the Titan X Pascal GPU used for this research.
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Georgakopoulos, S.V., Kottari, K., Delibasis, K., Plagianakos, V.P., Maglogiannis, I. (2017). Detection of Malignant Melanomas in Dermoscopic Images Using Convolutional Neural Network with Transfer Learning. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_34
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