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EEG-Based Biometric Verification Using Siamese CNNs Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Skin Lesions Classification: A Radiomics Approach with Deep CNN

Authors : Gabriele Piantadosi, Giampaolo Bovenzi, Giuseppe Argenziano, Elvira Moscarella, Domenico Parmeggiani, Ludovico Docimo, Carlo Sansone

Published in: New Trends in Image Analysis and Processing – ICIAP 2019

Publisher: Springer International Publishing

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Abstract

Supporting the early diagnosis of skin cancer is crucial for the sake of any kind of treatment or surgery. This work proposes to improve the outcome of automatic diagnoses approaches by using an ensemble of pre-trained deep convolutional neural networks and a suitable voting strategy. Moreover, a novel patching approach has been deployed. The proposal has been fairly evaluated with the literature proposals demonstrating good preliminary results.

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Footnotes
Literature
1.
Argenziano, G., et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the internet. J. Am. Acad. Dermatol. 48(5), 679–693 (2003)CrossRef
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Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)CrossRef
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Haenssle, H.A., et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann. Oncol. 29(8), 1836–1842 (2018)CrossRef
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
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Henning, J.S., et al.: The cash (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. J. Am. Acad. Dermatol. 56(1), 45–52 (2007)CrossRef
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Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
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Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Trans. Circuits Syst. Video Technol. 11(6), 703–715 (2001)CrossRef
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Marghoob, A.A., et al.: Instruments and new technologies for the in vivo diagnosis of melanoma. J. Am. Acad. Dermatol. 49(5), 777–797 (2003)CrossRef
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Menzies, S., Ingvar, C., McCarthy, W.: A sensitivity and specificity analysis of the surface microscopy features of invasive melanoma. Melanoma Res. 6(1), 55–62 (1996)CrossRef
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Pomponiu, V., Nejati, H., Cheung, N.M.: Deepmole: deep neural networks for skin mole lesion classification. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 2623–2627. IEEE (2016)
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Yu, H., Li, M., Zhang, H.J., Feng, J.: Color texture moments for content-based image retrieval. In: Proceedings of International Conference on Image Processing, vol. 3, pp. 929–932. IEEE (2002)
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Zalaudek, I., et al.: Three-point checklist of dermoscopy: an open internet study. Br. J. Dermatol. 154(3), 431–437 (2006)CrossRef
Metadata
Title
Skin Lesions Classification: A Radiomics Approach with Deep CNN
Authors
Gabriele Piantadosi
Giampaolo Bovenzi
Giuseppe Argenziano
Elvira Moscarella
Domenico Parmeggiani
Ludovico Docimo
Carlo Sansone
Copyright Year
2019
Book
New Trends in Image Analysis and Processing – ICIAP 2019

Print ISBN: 978-3-030-30753-0

Electronic ISBN: 978-3-030-30754-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-30754-7_26

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