Abstract
Early diagnosis of skin lesions is essential for the positive outcome of the disease, which can only be resolved with surgical treatment. In this manuscript, a deep learning method is proposed for the classification of cutaneous lesions based on their visual appearance and on the patient’s anamnestic data. These include age and gender of the patient and position of the lesion. The classifier discriminates between benign and malignant lesions, mimicking a typical procedure in dermatological diagnostics. Good preliminary results on the ISIC Dataset demonstrate the importance of the information fusion process, which significantly improves the classification accuracy.
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
Notes
References
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Chen, L.C., et al.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Andreini, P., Bonechi, S., Bianchini, M., Mecocci, A., Scarselli, F.: A deep learning approach to bacterial colony segmentation. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 522–533. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01424-7_51
Rossi, A., et al.: Analysis of brain NMR images for age estimation with deep learning
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)
Yap, J., Yolland, W., Tschandl, P.: Multimodal skin lesion classification using deep learning. Exp. Dermatol. 27(11), 1261–1267 (2018)
Leiter, U., Eigentler, T., Garbe, C.: Epidemiology of skin cancer. Adv. Exp. Med. Biol. 810, 120–140 (2014)
Apalla, Z., et al.: Skin cancer: epidemiology, disease burden, pathophysiology, diagnosis, and therapeutic approaches. Dermatol. Ther. 7(1), 5–19 (2017)
Paolino, G., et al.: Histology of non-melanoma skin cancers: an update. Biomedicines 5(4), 71 (2017)
Apalla, Z., et al.: Epidemiological trends in skin cancer. Dermatol. Pract. Conceptual 7(2), 1 (2017)
Rastrelli, M., et al.: Melanoma: epidemiology, risk factors, pathogenesis, diagnosis and classification. Vivo 28(6), 1005–1011 (2014)
Schadendorf, D., Hauschild, A.: Melanoma in 2013: melanoma—the run of success continues. Nat. Rev. Clin. Oncol. 11(2014), 75–76 (2013)
Globocan. https://gco.iarc.fr/. Accessed 06 June 2019
Matthews, N.H., et al.: Epidemiology of melanoma (2017)
Domingues, B., et al.: Melanoma treatment in review. ImmunoTargets Ther. 7, 35 (2018)
Gandini, S., et al.: Meta-analysis of risk factors for cutaneous melanoma: II. Sun exposure. Eur. J. Cancer 41(1), 45–60 (2005)
Codella, N.C., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by ISIC. In: 15th International Symposium on Biomedical Imaging, pp. 168–172. IEEE (2018)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Tognetti, L., et al.: An integrated clinical-dermoscopic risk scoring system for the differentiation between early melanoma and atypical nevi: the iDScore. J. Eur. Acad. Dermatol. Venereology 32(12), 2162–2170 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bonechi, S. et al. (2019). Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-30754-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30753-0
Online ISBN: 978-3-030-30754-7
eBook Packages: Computer ScienceComputer Science (R0)
