Identifying melanoma among benign simulators – Is there a role for deep learning convolutional neural networks? (MelSim Study)

Collaboration with a DL-CNN slightly improved dermatologists' diagnostic accuracy in a mixed CM and MelSim dataset, by increasing sensitivity without a loss of specificity. The DL-CNN's level of sensitivity in this difficult-to-diagnose dataset underlines the potential as an assistant diagnostic tool.

Authors

Alexander Enk (ORCID: https://orcid.org/0000-0002-1816-421X)
J. Winkler
Katharina Kommoss
Holger Andreas Haenssle (ORCID: https://orcid.org/0000-0001-7255-3104)
Anastasia Sophie Vollmer (ORCID: https://orcid.org/0000-0003-4200-9647)
Andreas Blum (ORCID: https://orcid.org/0000-0003-4087-5015)
W Stolz

Institutions

Heidelberg University (DE)
University Hospital Heidelberg (DE)
München Klinik (DE)
Klinikum Konstanz (DE)

Publication Details

Journal: European Journal of Cancer
Published: 2025-08-10
DOI: https://doi.org/10.1016/j.ejca.2025.115706
Citations: 1
Primary Topic: Cutaneous Melanoma Detection and Management
Type: article
Field-Weighted Citation Impact: 1.05

Funders

Universität Heidelberg

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article

Identifying melanoma among benign simulators – Is there a role for deep learning convolutional neural networks? (MelSim Study)

Alexander Enk, J. Winkler, Katharina Kommoss, Holger Andreas Haenssle et al.

Aug 10, 2025

1 citations

European Journal of Cancer

Cutaneous Melanoma Detection and Management

1.05

article

Identifying melanoma among benign simulators – Is there a role for deep learning convolutional neural networks? (MelSim Study)

Alexander Enk, J. Winkler, Katharina Kommoss, Holger Andreas Haenssle, Anastasia Sophie Vollmer, Andreas Blum, W Stolz

August 10, 2025article en

1 citations

Abstract

Collaboration with a DL-CNN slightly improved dermatologists' diagnostic accuracy in a mixed CM and MelSim dataset, by increasing sensitivity without a loss of specificity. The DL-CNN's level of sensitivity in this difficult-to-diagnose dataset underlines the potential as an assistant diagnostic tool.

European Journal of CancerVol. 227

Heidelberg University (DE), University Hospital Heidelberg (DE), München Klinik (DE), Klinikum Konstanz (DE)

Universität Heidelberg

Openalex Percentile: Top 19%

Cutaneous Melanoma Detection and Management

1.05

DOI Open Access PDF OpenAlex

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Identifying melanoma among benign simulators – Is there a role for deep learning convolutional neural networks? (MelSim Study) — Alexander Enk, J. Winkler, et al. · European Journal of Cancer (2025) | TGRS Research Map | TGRS