Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network

Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network

Convolutional neural networks (CNNs) have shown promise in discriminating between invasive and in situ melanomas. The aim of this study was to analyse how a CNN model, integrating both clinical close-up and dermoscopic images, performed compared with 6 independent dermatologists. The secondary aim w...

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Bibliographic Details
Main Authors: Martin Gillstedt, Ludwig Mannius, John Paoli, Johan Dahlén Gyllencreutz, Julia Fougelberg, Eva Johansson Backman, Jenna Pakka, Oscar Zaar, Sam Polesie
Format: Article
Language:English
Published: Medical Journals Sweden 2022-10-01
Series:Acta Dermato-Venereologica
Subjects:
artificial intelligence
clinical decision-making
melanoma
neural networks, computer
supervised machine learning
Online Access:https://medicaljournalssweden.se/actadv/article/view/2681

Internet

https://medicaljournalssweden.se/actadv/article/view/2681

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