Wang Jiaoju, Luo Yan, Wang Zheng, Hounye Alphonse Houssou, Cao Cong, Hou Muzhou, Zhang Jianglin
School of Mathematics and Statistics, Central South University, Changsha, 410083 Hunan China.
Department of dermatology of Xiangya hospital, Central South University, Changsha, 410083 Hunan China.
Appl Intell (Dordr). 2023;53(7):7614-7633. doi: 10.1007/s10489-022-03774-z. Epub 2022 Jul 29.
Acne vulgaris, the most common skin disease, can cause substantial economic and psychological impacts to the people it affects, and its accurate grading plays a crucial role in the treatment of patients. In this paper, we firstly proposed an acne grading criterion that considers lesion classifications and a metric for producing accurate severity ratings. Due to similar appearance of acne lesions with comparable severities and difficult-to-count lesions, severity assessment is a challenging task. We cropped facial skin images of several lesion patches and then addressed the acne lesion with a lightweight acne regular network (Acne-RegNet). Acne-RegNet was built by using a median filter and histogram equalization to improve image quality, a channel attention mechanism to boost the representational power of network, a region-based focal loss to handle classification imbalances and a model pruning and feature-based knowledge distillation to reduce model size. After the application of Acne-RegNet, the severity score is calculated, and the acne grading is further optimized by the metadata of the patients. The entire acne assessment procedure was deployed to a mobile device, and a phone app was designed. Compared with state-of-the-art lightweight models, the proposed Acne-RegNet significantly improves the accuracy of lesion classifications. The acne app demonstrated promising results in severity assessments (accuracy: 94.56%) and showed a dermatologist-level diagnosis on the internal clinical dataset.The proposed acne app could be a useful adjunct to assess acne severity in clinical practice and it enables anyone with a smartphone to immediately assess acne, anywhere and anytime.
寻常痤疮是最常见的皮肤病，会对患者造成重大的经济和心理影响，其准确分级在患者治疗中起着关键作用。在本文中，我们首先提出了一种痤疮分级标准，该标准考虑了皮损分类以及用于生成准确严重程度评分的度量方法。由于严重程度相当的痤疮皮损外观相似且皮损数量难以计数，严重程度评估是一项具有挑战性的任务。我们裁剪了几个皮损斑块的面部皮肤图像，然后使用轻量级痤疮正则网络（Acne-RegNet）处理痤疮皮损。Acne-RegNet通过使用中值滤波器和直方图均衡化来提高图像质量，使用通道注意力机制来增强网络的表征能力，使用基于区域的焦点损失来处理分类不均衡问题，以及使用模型剪枝和基于特征的知识蒸馏来减小模型大小。在应用Acne-RegNet之后，计算严重程度得分，并通过患者的元数据进一步优化痤疮分级。整个痤疮评估程序被部署到移动设备上，并设计了一个手机应用程序。与最先进的轻量级模型相比，所提出的Acne-RegNet显著提高了皮损分类的准确性。该痤疮应用程序在严重程度评估中显示出有前景的结果（准确率：94.56%），并且在内部临床数据集上显示出皮肤科医生水平的诊断。所提出的痤疮应用程序可以成为临床实践中评估痤疮严重程度的有用辅助工具，并且使任何拥有智能手机的人都能够随时随地立即评估痤疮。
