A multi-class skin Cancer classification using deep convolutional neural networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-020-09388-2.pdf
Reference83 articles.
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2. Alom, MZ, Aspiras, T, Taha, TM, & Asari, VK (2020). Skin cancer segmentation and classification with improved deep convolutional neural network. In: Medical Imaging 2020: Imaging informatics for healthcare, research, and applications, vol. 11318, pp. 1131814. International Society for Optics and Photonics. doi: https://doi.org/10.1117/12.2550146.
3. Australian Government (2018). Melanoma of the skin statistics. https://melanoma.canceraustralia.gov.au/statistics. Accessed 19 June 2019.
4. Ballerini L, Fisher RB, Aldridge B, Rees J (2013) A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Color medical image analysis. Springer, Dordrecht, pp 63–86
5. Binder M, Schwarz M, Winkler A, Steiner A, Kaider A, Wolff K, Pehamberger H (1995) Epiluminescence microscopy: a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Arch Dermatol 131(3):286–291
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