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

ABSTRACT: Background: Diagnosis of skin diseases is often challenging and computer-aided diagnostic tools are urgently needed to underpin decision making. Objective: To develop a convolutional neural network model to classify clinically relevant selected multiple-lesion skin diseases, this in accordance to the STARD guidelines. Methods: 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 images. Image data was distributed in training set (80%), validation set (10%), and test set (10%). All images were collected from a clinical database of a Danish population attending one dermatological department. Included was patients categorized with ICD-10 codes related to acne, rosacea, psoria

SUBMITTER: Thomsen K 

PROVIDER: S-EPMC7536339 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature