Facial analysis has emerged as a prominent area of research with diverse applications, including cosmetic surgery programs, the beauty industry, photography, and entertainment. Manipulating patient images often necessitates professional image processing software. This study contributes by providing a model that facilitates the detection of blemishes and skin lesions on facial images through a convolutional neural network and machine learning approach. The proposed method offers advantages such as simple architecture, speed and suitability for image processing while avoiding the complexities associated with traditional methods. The model comprises four main steps: area selection, scanning the chosen region, lesion diagnosis, and marking the identified lesion. Raw data for this research were collected from a reputable clinic in Tehran specializing in skincare and beauty services. The dataset includes administrative information, clinical data, and facial and profile images. A total of 2300 patient images were extracted from this raw data. A software tool was developed to crop and label lesions, with input from two treatment experts. In the lesion preparation phase, the selected area was standardized to 50 * 50 pixels. Subsequently, a convolutional neural network model was employed for lesion labeling. The classification model demonstrated high accuracy, with a measure of 0.98 for healthy skin and 0.97 for lesioned skin specificity. Internal validation involved performance indicators and cross-validation, while external validation compared the model's performance indicators with those of the transfer learning method using the Vgg16 deep network model. Compared to existing studies, the results of this research showcase the efficacy and desirability of the proposed model and methodology.

翻译：面部分析已成为一个重要的研究领域，具有多样化应用，包括美容外科程序、美妆行业、摄影和娱乐。处理患者图像通常需要专业图像处理软件。本研究通过提供一种基于卷积神经网络和机器学习方法的模型，促进面部图像上的瑕疵和皮肤病变检测。该方法具有架构简单、处理速度快且适合图像处理的优势，同时避免了传统方法的复杂性。该模型包含四个主要步骤：区域选择、选定区域扫描、病变诊断以及标识检测到的病变。本研究的原始数据来源于德黑兰一家知名的皮肤护理与美容服务机构。数据集包括管理信息、临床数据以及面部和侧面图像。从原始数据中提取了总共2300张患者图像。开发了一个软件工具，在两位治疗专家的参与下对病变进行裁剪和标注。在病变准备阶段，选定区域被标准化为50×50像素。随后，采用卷积神经网络模型进行病变标注。该分类模型显示出高精度，对于健康皮肤的灵敏度达0.98，对于病变皮肤的特性达0.97。内部验证涉及性能指标和交叉验证，而外部验证则将模型性能指标与使用Vgg16深度网络模型的迁移学习方法进行了比较。与现有研究相比，本研究结果展示了所提出模型及方法的有效性和优越性。