Open AccessRos‐NET: A deep convolutional neural network for automatic identification of rosacea lesions
Author(s) -
Binol Hamidullah,
Plotner Alisha,
Sopkovich Jennifer,
Kaffenberger Benjamin,
Niazi Muhammad Khalid Khan,
Gurcan Metin N.
Publication year - 2020
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/srt.12817
Subject(s) - rosacea , artificial intelligence , convolutional neural network , computer science , deep learning , erythema , pattern recognition (psychology) , computer vision , dermatology , medicine , acne
Background Rosacea is one of the most common cutaneous disorder characterized primarily by facial flushing, erythema, papules, pustules, telangiectases, and nasal swelling. Diagnosis of rosacea is principally done by a physical examination and a consistent patient history. However, qualitative human assessment is often subjective and suffers from a relatively high intra‐ and inter‐observer variability in evaluating patient outcomes. Materials and Methods To overcome these problems, we propose a quantitative and reproducible computer‐aided diagnosis system, Ros‐NET, which integrates information from different image scales and resolutions in order to identify rosacea lesions. This involves adaption of Inception‐ResNet‐v2 and ResNet‐101 to extract rosacea features from facial images. Additionally, we propose to refine the detection results by means of facial‐landmarks–based zones (ie, anthropometric landmarks) as regions of interest (ROI), which focus on typical areas of rosacea occurrence on a face. Results Using a leave‐one‐patient‐out cross‐validation scheme, the weighted average Dice coefficients, in percentages, across all patients (N = 41) with 256 × 256 image patches are 89.8 ± 2.6% and 87.8 ± 2.4% with Inception‐ResNet‐v2 and ResNet‐101, respectively. Conclusion The findings from this study support that pre‐trained networks trained via transfer learning can be beneficial in identifying rosacea lesions. Our future work will involve expanding the work to a larger database of cases with varying degrees of disease characteristics.