A novel deep learning framework is proposed for lesion segmentation and classification. The proposed technique incorporates two primary phases. For lesion segmentation, Mask recurrent convolutional neural network (MASK R-CNN) based architecture is implemented. In this architecture, Resnet50 along with feature pyramid network (FPN) is utilized as a backbone. Later, fully connected layer-based features are mapped for the final mask generation. In the classification phase, 24-layered convolutional neural network architecture is designed, which performs activation based on the visualization of higher features. Finally, best CNN features are provided to softmax classifiers for final classification. Three datasets (i.e. PH2, ISBI2016, and ISIC2017) are utilized for the validation of the segmentation process, whilst HAM10000 dataset is utilized for the classification. From the results, it is concluded that the proposed method outperforms several existing techniques, based on the selected set of parameters including sensitivity (85.57%), precision (87.01%), F1- Score (86.28%), and accuracy (86.5%).

提出了一种新颖的深度学习框架，用于病变分割和分类。所提出的技术包括两个主要阶段。对于病变分割，实现了基于Mask递归卷积神经网络（MASK R-CNN）的体系结构。在此体系结构中，Resnet50与特征金字塔网络（FPN）一起用作骨干网。后来，将完全连接的基于图层的特征映射为最终的蒙版生成。在分类阶段，设计了24层卷积神经网络架构，该架构基于更高特征的可视化执行激活。最后，最好的CNN功能会提供给softmax分类器以进行最终分类。三个数据集（即PH2，ISBI2016和ISIC2017）用于验证细分过程，而使用HAM10000数据集进行分类。从结果可以得出结论，基于选定的参数集，该方法优于几种现有技术，这些参数包括灵敏度（85.57％），精度（87.01％），F1-得分（86.28％）和准确性（86.5％） 。