Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases.
Ontology highlight
ABSTRACT:
SUBMITTER: Thomsen K
PROVIDER: S-EPMC7536339 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases.
Thomsen Kenneth K Christensen Anja Liljedahl AL Iversen Lars L Lomholt Hans Bredsted HB Winther Ole O
Frontiers in medicine 20200922
<b>Background:</b> Diagnosis of skin diseases is often challenging and computer-aided diagnostic tools are urgently needed to underpin decision making. <b>Objective:</b> To develop a convolutional neural network model to classify clinically relevant selected multiple-lesion skin diseases, this in accordance to the STARD guidelines. <b>Methods:</b> This was an image-based retrospective study using multi-task learning for binary classification. A VGG-16 model was trained on 16,543 non-standardized ...[more]
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