ResNet50 and VGG-16 models are introduced in this paper with different strategies, with and without preprocessing and with and without Support Vector Machine (SVM). Moreover, both transfer learning and data augmentation are used to solve the problem of lack of tagged data. The fully connected (FC) layer is replaced by the SVM classifier leading to better accuracy. In addition, in our work, we utilize the median filter, contrast enhancement and edge detection, which based on four main steps: noise removal, gradient smoothed image calculations, non-highest suppression and hysteresis thresholding. Also, the k-fold cross validation is performed to authenticate our model’s performance. Three data sets: ISIC 2017 MNIST-HAM10000 and ISBI 2016 are utilized in our proposed work. It is observed that the proposed technique of employing ResNet50 hybridized with SVM achieves the best performance, specifically with the ISIC2017 dataset, producing 99.19% accuracy, 99.32% area under the curve (AUC), 98.98% sensitivity, 98.78% precision, 98.88% F1 score and 2.6988 s computational time.

本文介绍了具有不同策略的ResNet50和VGG-16模型，无论有无预处理以及有无支持向量机（SVM）。此外，转移学习和数据扩充都用于解决缺少标记数据的问题。SVM分类器取代了全连接（FC）层，从而提高了准确性。另外，在我们的工作中，我们利用中值滤波器，对比度增强和边缘检测，它们基于四个主要步骤：噪声消除，梯度平滑图像计算，非最高抑制和磁滞阈值。同样，执行k倍交叉验证以验证我们模型的性能。我们建议的工作中使用了三个数据集：ISIC 2017 MNIST-HAM10000和ISBI 2016。