Abstract
Supporting the early diagnosis of skin cancer is crucial for the sake of any kind of treatment or surgery. This work proposes to improve the outcome of automatic diagnoses approaches by using an ensemble of pre-trained deep convolutional neural networks and a suitable voting strategy. Moreover, a novel patching approach has been deployed. The proposal has been fairly evaluated with the literature proposals demonstrating good preliminary results.
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Acknowledgments
The authors gratefully acknowledge the support of the Calculation Centre SCoPE of the University of Naples Federico II and his staff. This work is part of the “Synergy-net: Research and Digital Solutions against Cancer” project (funded in the framework of the POR Campania FESR 2014–2020 - CUP B61C17000090007).
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Piantadosi, G. et al. (2019). Skin Lesions Classification: A Radiomics Approach with Deep CNN. 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_26
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