Department of Computer Science, University of Management and Technology, Lahore 54000, Pakistan.
Department of Software Engineering, University of Management and Technology, Lahore 54000, Pakistan.
Sensors (Basel). 2022 Jul 28;22(15):5652. doi: 10.3390/s22155652.
Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers.
皮肤癌是一种致命的疾病，早期诊断可以提高生存机会。近年来，深度学习算法在皮肤癌检测中变得越来越流行。本研究提出了一种基于深度学习的新框架，用于对黑色素瘤、黑色素细胞痣、基底细胞癌和良性角化病等皮肤癌类型进行多分类。所提出的模型名为 SCDNet，它结合了 Vgg16 和卷积神经网络（CNN），用于对不同类型的皮肤癌进行分类。此外，还将所提出的方法与四个在医学领域中最先进的预训练分类器（Resnet 50、Inception v3、AlexNet 和 Vgg19）进行了准确性比较。使用 ISIC 2019 数据集评估了所提出的 SCDNet 分类器和四个最先进的分类器的性能。所提出的 SCDNet 在皮肤癌的多分类中的准确率为 96.91%，而 Resnet 50、Alexnet、Vgg19 和 Inception-v3 的准确率分别为 95.21%、93.14%、94.25%和 92.54%。结果表明，所提出的 SCDNet 比竞争分类器表现更好。
