Cargando…
Design of a Machine Learning System to Predict the Thickness of a Melanoma Lesion in a Non-Invasive Way from Dermoscopic Images
OBJECTIVES: Melanoma is the deadliest form of skin cancer, but it can be fully cured through early detection and treatment in 99% of cases. Our aim was to develop a non-invasive machine learning system that can predict the thickness of a melanoma lesion, which is a proxy for tumor progression, throu...
Autores principales: | Szijártó, Ádám, Somfai, Ellák, Lőrincz, András |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Medical Informatics
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10209725/ https://www.ncbi.nlm.nih.gov/pubmed/37190735 http://dx.doi.org/10.4258/hir.2023.29.2.112 |
Ejemplares similares
-
Multiple primary thick melanomas: similar dermoscopic pattern
por: Feci, Luca, et al.
Publicado: (2014) -
Dermoscopic Predictors of Tumor Thickness in Cutaneous Melanoma: A Retrospective Analysis of 245 Melanomas
por: Rodríguez-Lomba, Enrique, et al.
Publicado: (2021) -
Skin lesion classification of dermoscopic images using machine learning and convolutional neural network
por: Shetty, Bhuvaneshwari, et al.
Publicado: (2022) -
Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network
por: GILLSTEDT, Martin, et al.
Publicado: (2022) -
Interobserver Agreement on Dermoscopic Features and their Associations with In Situ and Invasive Cutaneous Melanomas
por: POLESIE, Sam, et al.
Publicado: (2021)