Department of Biomedical Engineering, Engineering Faculty, Akdeniz University, Turkey.
Comput Biol Med. 2021 Jan;128:104118. doi: 10.1016/j.compbiomed.2020.104118. Epub 2020 Nov 13.
Common properties of dermatological diseases are mostly lesions with abnormal pattern and skin color (usually redness). Therefore, dermatology is one of the most appropriate areas in medicine for automated diagnosis from images using pattern recognition techniques to provide accurate, objective, early diagnosis and interventions. Also, automated techniques provide diagnosis without depending on location and time. In addition, the number of patients in dermatology departments and costs of dermatologist visits can be reduced. Therefore, in this work, an automated method is proposed to classify dermatological diseases from color digital photographs. Efficiency of the proposed approach is provided by 2 stages. In the 1st stage, lesions are detected and extracted by using a variational level set technique after noise reduction and intensity normalization steps. In the 2nd stage, lesions are classified using a pre-trained DenseNet201 architecture with an efficient loss function. In this study, five common facial dermatological diseases are handled since they also cause anxiety, depression and even suicide death. The main contributions provided by this work can be identified as follows: (i) A comprehensive survey about the state-of-the-art works on classifications of dermatological diseases using deep learning; (ii) A new fully automated lesion detection and segmentation based on level sets; (iii) A new adaptive, hybrid and non-symmetric loss function; (iv) Using a pre-trained DenseNet201 structure with the new loss function to classify skin lesions; (v) Comparative evaluations of ten convolutional networks for skin lesion classification. Experimental results indicate that the proposed approach can classify lesions with high performance (95.24% accuracy).
皮肤病的共同特征主要是形态和肤色异常的病变（通常为红色）。因此，皮肤病学是医学中最适合使用模式识别技术从图像中进行自动诊断的领域之一，以提供准确、客观、早期的诊断和干预。此外，自动化技术可以在不依赖位置和时间的情况下进行诊断。此外，还可以减少皮肤科就诊的患者数量和皮肤科医生的就诊次数。因此，在这项工作中，提出了一种从彩色数字照片中自动分类皮肤病的方法。该方法通过两个阶段来提高效率。在第 1 阶段，通过噪声降低和强度归一化步骤后，使用变分水平集技术检测和提取病变。在第 2 阶段，使用经过预训练的 DenseNet201 架构和有效的损失函数对病变进行分类。在这项研究中，处理了五种常见的面部皮肤病，因为它们也会引起焦虑、抑郁，甚至自杀死亡。这项工作的主要贡献可以归纳为以下几点：（i）对使用深度学习进行皮肤病分类的最新技术进行了全面调查；（ii）提出了一种新的基于水平集的全自动病变检测和分割方法；（iii）提出了一种新的自适应、混合和非对称损失函数；（iv）使用经过预训练的 DenseNet201 结构和新的损失函数对皮肤病变进行分类；（v）对十种用于皮肤病变分类的卷积网络进行了比较评估。实验结果表明，所提出的方法可以实现高性能的病变分类（准确率为 95.24%）。
