Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

Kenneth Græbild Thomsen, Anja Liljedahl Christensen, Lars Iversen, Hans Bredsted Lomholt, Ole Winther

Frontiers in Medicine 2020 article cited by 68

importance 5/10 ai assisted Cutaneous Melanoma Detection and Management dermatology stage: research open-source model

Diagnosing multiple-lesion skin diseases such as acne, rosacea, psoriasis, eczema and cutaneous T-cell lymphoma is challenging and needs computer-aided support.

Misdiagnosis of skin diseases, including serious cutaneous T-cell lymphoma, can delay proper treatment.

A VGG-16 convolutional neural network using multi-task learning was trained on 16,543 non-standardized clinical images for binary classification of selected skin diseases.

Acne vs rosacea: sensitivity 85.42% (CI 72.24-93.93%), specificity 89.53% (CI 83.97-93.68%); CTCL comparison results truncated.

Shows a CNN can distinguish selected skin diseases from clinical images in one Danish single-center cohort.

Venue Frontiers in Medicine
Published 2020
Work type article
Authors Kenneth Græbild Thomsen, Anja Liljedahl Christensen, Lars Iversen, Hans Bredsted Lomholt, Ole Winther
Citations 68 (OpenAlex, at import time)
References 33
Model or method VGG-16 (CNN)
Organization not stated in source
Primary source https://doi.org/10.3389/fmed.2020.574329
Open access gold
Keywords deep learning, CNN, VGG-16, skin diseases, diagnosis
5 / 10

real but narrower advance

Computer-aided diagnosis of multiple-lesion skin diseases, including serious CTCL, is a real but narrower advance; no burden scale stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: 16,543 clinical images, single Danish dermatology department. Method and limits.

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