Skin cancer disease images classification using deep learning solutions
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/s11042-021-10952-7.pdf
Reference72 articles.
1. Abbas Q, Garcia IF, Celebi ME, Ahmad W, Mushtaq Q (2013) Unified approach for lesion border detection based on mixture modeling and local entropy thresholding. Skin Res Technol 19:314–319. https://doi.org/10.1111/srt.12047
2. Agilandeeswari L, Sagar MT, Keerthana N (2019) Skin lesion detection using texture based segmentation and classification by convolutional neural networks (CNN). Int J Innov Technol Exploring Eng 9:2117–2120. https://doi.org/10.35940/ijitee.B7085.129219
3. Attaran M, Deb P (2018) Machine learning: the new ‘big thing’ for competitive advantage. Int J Knowledge Eng Data Mining 5:277–305
4. Bajwa MN, Muta K, Malik MI, Siddiqui SA, Braun SA, Homey B, Dengel A, Ahmed S (2020) Computer-aided diagnosis of skin diseases using deep neural networks. Appl Sci 10:1–13. https://doi.org/10.3390/app10072488
5. Barbu T (2003) A pattern recognition approach to image segmentation. Proc Romanian Acad Ser A 4:143–148
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