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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Reed, K.B., Brewer, J.D., Lohse, C.M., Bringe, K.E., Pruit, C.N., Gibson, L.E.: increasing incidence of melanoma among young adults: an epidemiological study in Olmsted County, Minnesota. Mayo Clin. Proc. 87(4), 328–334 (2012)
Stern, R.S.: Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch. Dermatol. 146(3), 279–282 (2010)
Rogers, H.W., Weinstock, M.A., Harris, A.R., et al.: Incidence estimate of nonmelanoma skin cancer in the United States, 2006. Arch. Dermatol. 146(3), 283–287 (2010)
American Cancer Society. Cancer Facts & Figures (2015). http://www.cancer.org/research/cancerfactsstatistics/cancerfactsfigures2015/ Accessed 12 May 2015
Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf Technol. Biomed. 13(5), 721–733 (2009)
Menzies, S.W.: Cutaneous melanoma: making a clinical diagnosis, present and future. Dermatol. Ther. 19(1), 32–39 (2006)
Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions a review. Artif. Intell. Med. 56(2), 69–90 (2012)
Maglogiannis, I., Delibasis, K.: Enhancing classification accuracy utilizing globules and dots features in digital dermoscopy. Comput. Methods Progr. Biomed. 118(2), 124–133 (2015). ISSN 0169-2607
Dreiseitl, S., Ohno-Machado, L., Kittler, H., Vinterbo, S., Billhardt, H., Binder, M.: A comparison of machine learning methods for the diagnosis of pigmented skin lesions. J. Biomed. Inf. 34(1), 28–36 (2001)
Maglogiannis, I., Zafiropoulos, E.: Utilizing support vector machines for the characterization of digital medical images. BMC Med. Inform. Decis. Mak. 4(4) (2004). http://www.biomedcentral.com/content/pdf/1472-6947-4-4.pdf
Maragoudakis, M., Maglogiannis, I.: Skin lesion diagnosis from images using novel ensemble classification techniques. In: 2010 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB), pp. 1–5). IEEE, November 2010
Delibasis, K., Kottari, K., Maglogiannis, I.: Automated detection of streaks in dermoscopy images. In: Chbeir, R., Manolopoulos, Y., Maglogiannis, I., Alhajj, R. (eds.) AIAI 2015. IAICT, vol. 458, pp. 45–60. Springer, Cham (2015). doi:10.1007/978-3-319-23868-5_4
Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.: Oriented pattern analysis for streak detection in dermoscopy images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012 Part I. LNCS, vol. 7510, pp. 298–306. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33415-3_37
Sadeghi, M., Lee, T.K., McLean, D., Lui, H., Atkins, M.S.: Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans. Med. Imaging 32(5), 849–861 (2013)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Bottou, L.: On-line learning and stochastic approximations. In: On-line Learning in Neural Networks, pp. 9–42. Cambridge University Press (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc., New York (2012)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR09 (2009)
Georgakopoulos, S.V., Iakovidis, D.K., Vasilakakis, M., Plagianakos, V.P., Koulaouzidis, A.: Weakly-supervised convolutional learning for detection of inflammatory gastrointestinal lesions. In: 2016 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 510–514. October 2016
Iakovidis, D.K., Koulaouzidis, A.: Software for enhanced video capsule endoscopy: challenges for essential progress. Nat. Rev. Gastroenterol. Hepatol. 12(3), 172–186 (2015)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, vol. 27, pp. 3320–3328 (2014)
Zhang, R., Zheng, Y., Mak, T.W.C., Yu, R., Wong, S.H., Lau, J.Y.W., Poon, C.C.Y.: Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE J. Biomed. Health Inform. 21(1), 41–47 (2017)
Acknowledgments
We gratefully acknowledge the support of NVDIA Corporation for the donation of the Titan X Pascal GPU used for this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-65172-9_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65171-2
Online ISBN: 978-3-319-65172-9
eBook Packages: Computer ScienceComputer Science (R0)