Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features

Author:

Maurya Akanksha,Stanley R. JoeORCID,Aradhyula Hemanth Y.,Lama Norsang,Nambisan Anand K.,Patel Gehana,Saeed Daniyal,Swinfard Samantha,Smith Colin,Jagannathan Sadhika,Hagerty Jason R.,Stoecker William V.

Publisher

Springer Science and Business Media LLC

Reference31 articles.

1. H. W. Rogers, M. A. Weinstock, S. R. Feldman, and B. M. Coldiron, “Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the us population, 2012,” JAMA Dermatol, vol. 151, no. 10, pp. 1081–1086, 2015. https://doi.org/10.1001/jamadermatol.2015.1187.

2. R. L. Siegel, K. D. Miller, H. E. Fuchs, and A. Jemal, “Cancer statistics, 2021,” CA Cancer J Clin, vol. 71, no. 1, pp. 7–33, 2021.

3. A. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017. https://doi.org/10.1038/nature21056.

4. M. A. Marchetti, N. C. F. Codella, S. W. Dusza, D. A. Gutman, B. Helba, A. Kalloo, N. Mishra, C. Carrera, M. E. Celebi, J. L. DeFazio, N. Jaimes, A. A. Marghoob, E. Quigley, A. Scope, O. Yélamos, A. C. Halpern, & International Skin Imaging Collaboration 2018 Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images J Am Acad Dermatol 78 2 270 277. https://doi.org/10.1016/j.jaad.2017.08.016

5. H. A. Haenssle, C. Fink, F. Toberer, J. Winkler, W. Stolz, T. Deinlein, R. Hofmann-Wellenhof, A. Lallas, S. Emmert, T. Buhl, M. Zutt, A. Blum, M. S. Abassi, L. Thomas, I. Tromme, P. Tschandl, A. Enk, A. Rosenberger, & Reader Study Level I and Level II Groups, “Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions,” Annals of Oncology, vol. 31, no. 1, pp. 137–143, Jan. 2020, https://doi.org/10.1016/j.annonc.2019.10.013.

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