With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin cancer.For this purpose,this work investigates the capability of deep networks in diagnosing a va...With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin cancer.For this purpose,this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images.This paper aims to develop and ne-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images.Fine-tuning is a powerful method to obtain enhanced classication results by the customized pre-trained network.Regularization,batch normalization,and hyperparameter optimization are performed for ne-tuning the proposed deep network.The proposed ne-tuned ResNet50 model successfully classied 7-respective classes of dermoscopic lesions using the publicly available HAM10000 dataset.The developed deep model was compared against two powerful models,i.e.,InceptionV3 and VGG16,using the Dice similarity coefcient(DSC)and the area under the curve(AUC).The evaluation results show that the proposed model achieved higher results than some recent and robust models.展开更多
针对光伏板检测提取精度问题,提出一种改进的SSD和ResNet的光伏板检测与分类算法。为了提升SSD算法检测精度,在其主干网络VGG16中融合CBAM注意力机制,从而增强算法的多尺度特征提取能力,针对光伏板的形状特征,重新设计了网络中默认框的...针对光伏板检测提取精度问题,提出一种改进的SSD和ResNet的光伏板检测与分类算法。为了提升SSD算法检测精度,在其主干网络VGG16中融合CBAM注意力机制,从而增强算法的多尺度特征提取能力,针对光伏板的形状特征,重新设计了网络中默认框的长宽比;在ResNet算法每个残差结构中嵌入SENet(Squeeze and Excitation)通道注意力模块,提升模型特征提取能力。结果表明,改进后SSD算法检测精度更高,模型训练速度更快;改进后ResNet模型在光伏表面缺陷数据集上分类准确率较原算法有了很大提升。展开更多
文摘With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin cancer.For this purpose,this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images.This paper aims to develop and ne-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images.Fine-tuning is a powerful method to obtain enhanced classication results by the customized pre-trained network.Regularization,batch normalization,and hyperparameter optimization are performed for ne-tuning the proposed deep network.The proposed ne-tuned ResNet50 model successfully classied 7-respective classes of dermoscopic lesions using the publicly available HAM10000 dataset.The developed deep model was compared against two powerful models,i.e.,InceptionV3 and VGG16,using the Dice similarity coefcient(DSC)and the area under the curve(AUC).The evaluation results show that the proposed model achieved higher results than some recent and robust models.
文摘针对光伏板检测提取精度问题,提出一种改进的SSD和ResNet的光伏板检测与分类算法。为了提升SSD算法检测精度,在其主干网络VGG16中融合CBAM注意力机制,从而增强算法的多尺度特征提取能力,针对光伏板的形状特征,重新设计了网络中默认框的长宽比;在ResNet算法每个残差结构中嵌入SENet(Squeeze and Excitation)通道注意力模块,提升模型特征提取能力。结果表明,改进后SSD算法检测精度更高,模型训练速度更快;改进后ResNet模型在光伏表面缺陷数据集上分类准确率较原算法有了很大提升。
