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In Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) ; h5-index 0.0

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.

Binol Hamidullah, Plotner Alisha, Sopkovich Jennifer, Kaffenberger Benjamin, Niazi Muhammad Khalid Khan, Gurcan Metin N

2019-Dec-17

computer-assisted diagnosis, convolutional neural networks, deep learning, rosacea, semantic segmentation, transfer learning