# FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses
## 3 Materials and Methods  
### Methodology for Skin Rosacea Detection Combining the Five Best Classifiers  
The clinical images were obtained from 40 patients with rosacea and 59 control groups from Johns Hopkins University Hospital (Figure 1a,b). In order to improve the rosacea classification performance, we used multi-view clinical photos, which are photos of the same patient from different views. The high performance of multi-view, multi-modal, and integration approaches in machine learning and deep learning for image classification have been reported previously [32; 33]. The images in the region of interest were segmented with different shapes and sizes, and subsequently, the segmented images were augmented by rotating and scaling (Figure 1a). The augmentation options included random reflections based on the \(x\)-axis (i.e., horizontal flipping), random rotation within the rate [\(-\)90; 90], and random rescaling within the range [1; 2]. The resolution of the images was approximately 15-20 pixels/mm. A total of 600 images (66% of the entire data set) for each group were used for training through the well-known 19 pre-trained CNNs (Figure 1c). A total of 200 images (22%) for each group were utilized for validation, and 110 images (12%) were utilized as test images. After training the CNN-based models, all models were tested to classify images using the validation data set. Consequently, the top 5 CNN-based models were selected based on accuracy to apply them to FACES (Figure 1c) as follows: ResNet-101 [34] (accuracy: 90.75%), DarkNet-19 [35] (90.25%), DarkNet-53 [36] (89.5%), ResNet-50 [37] (89.0%), and GoogleNet [38] (88.5%) (Table 2). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Skin Lesions** & **References** \\ \hline U-Net, VGGNet, Inception-v3, & Rosacea, acne vulgaris, hemangiioma, psoriasis, and & \\ InceptionResNet-v2, and ResNet & seborrheic dermatitis & \\ \hline Inception-v3 & Rosacea, acne, atopic dermatitis, psoriasis, & \\  & and impetigo & \\ \hline DenseNet201 & Rosacea, acne vulgaris, hemangiioma, psoriasis, and & \\  & seborrheic dermatitis & \\ \hline VGG-16 & Rosacea, acne, psoriasis, eczema, and cutaneous & \\  & t-cell lymphoma & \\ \hline Ros-Net & Rosacea & [PERSON] et al. [14] \\ \hline Inception-ResNet-v2 & Rosacea & [PERSON] et al. [30] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous related studies for the identification of rosacea. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Depth** & **Size** & **Parameters (Millions)** & **Image Input Size** \\ \hline ResNet-101 & 101 & 167 MB & 44.6 & 224 by 224 \\ \hline DarkNet-19 & 19 & 78 MB & 20.8 & 256 by 256 \\ \hline DarkNet-53 & 53 & 155 MB & 41.6 & 256 by 256 \\ \hline ResNet-50 & 50 & 96 MB & 25.6 & 224 by 224 \\ \hline GoogleNet & 22 & 27 MB & 7.0 & 224 by 224 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Properties of the 5 used models for FACES. All of the selected 5 methods are CNN models that take a linear O(n) time, where n is the number of input pixels [39]. Floating point operations per second (FLOPS) reflects the computation complexity of CNN models. The FLOPSs for ResNet101, ResNet50, DarkNet53, DarkNet19, and GoogleNet are 7.6, 3.8, 53.6, 5.58, and 1.5 (billion), respectively. For our linear model, the total FLOPs are the summation of FLOPs of each selected method (i.e., 7.6 + 3.8 + 53.6 + 5.58 + 1.5 = 72.08 billion) because our methods are feedforward, traveling forward through the entire network. Stochastic gradient descent with a momentum (SGDM) optimizer was used as a solver. Different values of initial learning rate (0.0001-0.01), validation frequency (1-10), max epochs (5-50), and mini-batch sizes (3-30) were tested to find optimum values for the highest accuracy because different CNN models exhibited different optimized values. In addition, L2 regularization was applied with a value of 0.0001 as another hyperparameter.