Cargando…
Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope
BACKGROUND: Skin cancer (SC), especially melanoma, is a growing public health burden. Experimental studies have indicated a potential diagnostic role for deep learning (DL) algorithms in identifying SC at varying sensitivities. Previously, it was demonstrated that diagnostics by dermoscopy are impro...
Autores principales: | Dascalu, A., David, E.O. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6562065/ https://www.ncbi.nlm.nih.gov/pubmed/31101596 http://dx.doi.org/10.1016/j.ebiom.2019.04.055 |
Ejemplares similares
-
Non-melanoma skin cancer diagnosis: a comparison between dermoscopic and smartphone images by unified visual and sonification deep learning algorithms
por: Dascalu, A., et al.
Publicado: (2021) -
Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies
por: Walker, B.N., et al.
Publicado: (2019) -
Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images
por: Bechelli, Solene, et al.
Publicado: (2022) -
Triage amalgamated dermoscopic algorithm (TADA) for skin cancer screening
por: Rogers, Tova, et al.
Publicado: (2017) -
Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detection
por: Ningrum, Dina Nur Anggraini, et al.
Publicado: (2021)