# FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses
## 1 Introduction  
Rosacea is a chronic inflammatory skin disorder that causes visible blood vessels and redness on the human face [1]--a condition that affects more than 16 million Americans [2]. Globally, the prevalence of this skin condition is thought to range from 0.2% to 22% of the population in North America and Europe [3, 4]. Rosacea is categorized into four types: erythematosusolangiectatic, papulopustular, phytamous, and ocular rosacea [5, 6]. However, it is often difficult to distinguish one type from another [7]. In addition, visual assessment of rosacea, the standard clinical diagnostic method, is often subjective among clinicians and results in high variation [8, 9, 10]. As such, in many cases, rosacea is misdiagnosed as other skin disorders, such as acne, eczema, and lupus, or vice versa due to their similarities in appearance [11]. Over the past decade, advances in computer-aided diagnosis and treatment using artificial intelligence have allowed for the detection of various skin diseases with high accuracy and consistency [12]. Furthermore, due to the COVID-19 pandemic and self-isolation in patients, telemedicine, which doesn't require direct contact between cliniciansand patients, has gained popularity [13]. The skin diseases for deep-learning-based classification are diverse, ranging from cancer to acne, eczema, psoriasis, etc. However, the majority of the studies have been focused on skin cancer research [14; 15; 16; 17; 18; 19; 20; 21; 22]. [PERSON] et al. employed a pre-trained deep learning network such as AlexNet to classify three different lesions (melanoma, common nevus, and atypical nevus) [23]. [PERSON] et al. developed a new, effective decision system to distinguish melanoma from a nevus, which combines several deep learning and machine learning models [24]. They employed five well-known convolutional neural network (CNN) models to develop a global classifier, which is the methodology used in this study, but they did not investigate numerous CNN models to find the five best. Moreover, they only tested a linear function to calculate the global index of decision and only tested the evaluation factor value (i.e., \(\alpha\) = 0.7). [PERSON] et al. developed a new deep learning method for the effective detection of non-melanoma skin cancers, which are the most common skin cancers: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and intraepidermal carcinoma (IEC). [PERSON] et al. used both deep learning and machine learning models to detect diverse skin lesions, including melanoma [25]. This study utilized the International Skin Imaging Collaboration (ISIC) database to classify 2624 dermoscopic images based on a sparse coding, deep residual network, and support vector machine. The results display the high-performance values of classification, with 93.1% accuracy, 92.8% specificity, and 94.9% sensitivity. In addition to cancer, some efforts have been made to classify non-cancerous skin diseases through artificial intelligence, including psoriasis, atopic dermatitis, eczema, acne, hemangioma, onychomycosis, and so on [8; 26; 27]. [PERSON] et al. employed k-means clustering to classify acne lesions by collecting acne samples with different grades (mild, moderate, severe, and very severe) from 98 patients [28]. [PERSON] et al. trained TensorFlow Inception version 3 to recognize five dermatological diseases, including atopic dermatitis, acne, psoriasis, impetigo, and rosacea [29]. They measured six statistical parameters, such as sensitivity, specificity, PPV, NPN, MCC, and F1 score, with the application of data augmentation. [PERSON] et al. adopted a pre-trained deep model, VGG-16, for the classification of multiple skin diseases (acre, rosacea, psoriasis, eczema, and cutaneous t-cell lymphoma) [15]. [PERSON] tested the performance of several deep learning models (U-Net, InceptionV3, ResNet, InceptionResNetV2, and VGGNet) to classify five common skin disorders: acne vulgaris, hemangioma, psoriasis, rosacea, and seborrheic dermatitis.