Serrano Carmen, Lazo Manuel, Serrano Amalia, Toledo-Pastrana Tomás, Barros-Tornay Rubén, Acha Begoña
Dpto. Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain.
Hospital Universitario Virgen Macarena, Calle Dr. Fedriani, 3, 41009 Seville, Spain.
J Imaging. 2022 Jul 12;8(7):197. doi: 10.3390/jimaging8070197.
. Skin cancer is the most common cancer worldwide. One of the most common non-melanoma tumors is basal cell carcinoma (BCC), which accounts for 75% of all skin cancers. There are many benign lesions that can be confused with these types of cancers, leading to unnecessary biopsies. In this paper, a new method to identify the different BCC dermoscopic patterns present in a skin lesion is presented. In addition, this information is applied to classify skin lesions into BCC and non-BCC. . The proposed method combines the information provided by the original dermoscopic image, introduced in a convolutional neural network (CNN), with deep and handcrafted features extracted from color and texture analysis of the image. This color analysis is performed by transforming the image into a uniform color space and into a color appearance model. To demonstrate the validity of the method, a comparison between the classification obtained employing exclusively a CNN with the original image as input and the classification with additional color and texture features is presented. Furthermore, an exhaustive comparison of classification employing different color and texture measures derived from different color spaces is presented. . Results show that the classifier with additional color and texture features outperforms a CNN whose input is only the original image. Another important achievement is that a new color cooccurrence matrix, proposed in this paper, improves the results obtained with other texture measures. Finally, sensitivity of 0.99, specificity of 0.94 and accuracy of 0.97 are achieved when lesions are classified into BCC or non-BCC. . To the best of our knowledge, this is the first time that a methodology to detect all the possible patterns that can be present in a BCC lesion is proposed. This detection leads to a clinically explainable classification into BCC and non-BCC lesions. In this sense, the classification of the proposed tool is based on the detection of the dermoscopic features that dermatologists employ for their diagnosis.
皮肤癌是全球最常见的癌症。最常见的非黑色素瘤肿瘤之一是基底细胞癌（BCC），占所有皮肤癌的75%。有许多良性病变可能与这些类型的癌症混淆，导致不必要的活检。本文提出了一种识别皮肤病变中不同基底细胞癌皮肤镜模式的新方法。此外，该信息被用于将皮肤病变分类为基底细胞癌和非基底细胞癌。
所提出的方法将卷积神经网络（CNN）中引入的原始皮肤镜图像提供的信息与从图像的颜色和纹理分析中提取的深度和手工特征相结合。这种颜色分析是通过将图像转换为均匀颜色空间和颜色外观模型来进行的。为了证明该方法的有效性，给出了仅以原始图像作为输入的CNN分类与附加颜色和纹理特征的分类之间的比较。此外，还给出了使用从不同颜色空间导出的不同颜色和纹理度量进行分类的详尽比较。
结果表明，具有附加颜色和纹理特征的分类器优于仅以原始图像作为输入的CNN。另一个重要成果是，本文提出的一种新的颜色共生矩阵改进了使用其他纹理度量获得的结果。最后，当病变被分类为基底细胞癌或非基底细胞癌时，实现了0.99的灵敏度、0.94的特异性和0.97的准确率。
据我们所知，这是首次提出一种检测基底细胞癌病变中所有可能模式的方法。这种检测导致了对基底细胞癌和非基底细胞癌病变的临床可解释分类。从这个意义上说，所提出工具的分类基于皮肤科医生用于诊断的皮肤镜特征的检测。
