Department of Electronics, Sambalpur University, Sambalpur, Odisha, India.
Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India.
J Digit Imaging. 2022 Oct;35(5):1207-1216. doi: 10.1007/s10278-022-00632-9. Epub 2022 May 6.
The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance: brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: First, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM, and then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12,288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12,288. The highest results are obtained with 12,288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 and VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.
皮肤是人体最大的器官，成年人的皮肤平均重约 8 磅。我们的皮肤是一种非常奇妙的器官，它将我们与外界隔离，保护我们的身体免受伤害。然而，皮肤也很容易受到损伤，并且会失去原有的颜色：棕色、黑色或蓝色，或者这些颜色的组合，这些被称为色素性皮损。这些常见的色素性皮损（CPSL）是皮肤癌的主要因素，可以说是皮肤癌的主要原因。在医疗保健领域，由于输出不准确、过拟合和更高的计算成本，CPSL 的分类是主要问题。因此，我们提出了一种基于多深度特征和支持向量机（SVM）的 CPSL 分类模型。该系统包括两个阶段：首先，使用 SVM 评估 11 个 CNN 模型在深度特征提取方法中的性能，然后，将表现最好的三个 CNN 模型的深度特征进行串联，并借助 SVM 对 CPSL 进行分类。在第二步中，通过将表现最好的 CNN 模型的 4096 个特征的二进制和三进制网络组合，得到 8192 和 12288 个特征。这些特征也被提供给 SVM 分类器。SVM 结果也通过主成分分析（PCA）算法与 8192 和 12288 个特征的组合进行评估。使用 12288 个特征可获得最高的结果。实验结果表明，使用 SVM 分类器，Alexnet、VGG16 和 VGG19 的深度特征组合可获得最高的 91.7%准确率。因此，结果表明，所提出的方法是 CPSL 分类的有用工具。
