针对神经网络结构的特征提取能力不足以及在包含复杂图像特征的数据集上分类准确率不高的问题,本文提出了一种对MobileNet神经网络的改进策略(L-MobileNet)。将原标准卷积形式替换为深度可分离卷积形式,并将深度卷积层得到的特征图执行...针对神经网络结构的特征提取能力不足以及在包含复杂图像特征的数据集上分类准确率不高的问题,本文提出了一种对MobileNet神经网络的改进策略(L-MobileNet)。将原标准卷积形式替换为深度可分离卷积形式,并将深度卷积层得到的特征图执行取反操作,通过深度卷积融合层传递至下一层;采用Leaky ReLU激活函数代替原ReLU激活函数来保留图像中更多的正负特征信息,并加入类残差结构避免梯度弥散现象。与6种方法进行对比,实验结果表明:L-MobileNet在数据集Cifar-10、Cifar-100(coarse)、Cifar-100(fine)和Dogs vs Cats上平均准确率和最高准确率都取得了最佳结果。展开更多
(Aim)Chinese sign language is an essential tool for hearing-impaired to live,learn and communicate in deaf communities.Moreover,Chinese sign language plays a significant role in speech therapy and rehabilitation.Chine...(Aim)Chinese sign language is an essential tool for hearing-impaired to live,learn and communicate in deaf communities.Moreover,Chinese sign language plays a significant role in speech therapy and rehabilitation.Chinese sign language identification can provide convenience for those hearing impaired people and eliminate the communication barrier between the deaf community and the rest of society.Similar to the research of many biomedical image processing(such as automatic chest radiograph processing,diagnosis of chest radiological images,etc.),with the rapid development of artificial intelligence,especially deep learning technologies and algorithms,sign language image recognition ushered in the spring.This study aims to propose a novel sign language image recognition method based on an optimized convolutional neural network.(Method)Three different combinations of blocks:Conv-BN-ReLU-Pooling,Conv-BN-ReLU,Conv-BN-ReLU-BN were employed,including some advanced technologies such as batch normalization,dropout,and Leaky ReLU.We proposed an optimized convolutional neural network to identify 1320 sign language images,which was called as CNN-CB method.Totally ten runs were implemented with the hold-out randomly set for each run.(Results)The results indicate that our CNN-CB method gained an overall accuracy of 94.88±0.99%.(Conclusion)Our CNN-CB method is superior to thirteen state-of-the-art methods:eight traditional machine learning approaches and five modern convolutional neural network approaches.展开更多
眼底血管的健康状态对于研究各类眼科疾病具有重要的参考意义。为了帮助临床医疗人员对眼底微血管形态结构图像的分析来诊断疾病,文中提出了一种基于编码-解码(Encoder-Decoder)结构的U-net的眼底血管分割方法。首先,在模型训练之前对...眼底血管的健康状态对于研究各类眼科疾病具有重要的参考意义。为了帮助临床医疗人员对眼底微血管形态结构图像的分析来诊断疾病,文中提出了一种基于编码-解码(Encoder-Decoder)结构的U-net的眼底血管分割方法。首先,在模型训练之前对图像进行预处理,然后使用Leaky ReLU激活函数替换U-net ReLU,避免了神经元的死亡问题,同时使用Adam(Adaptive Moment Estimate)优化器代替梯度下降法优化学习策略,最后对血管分割的平均交并比进行计算评估。实验表明,优化后的模型的平均精度可达到93.29%,相比原算法提升了3.26%。展开更多
文摘针对神经网络结构的特征提取能力不足以及在包含复杂图像特征的数据集上分类准确率不高的问题,本文提出了一种对MobileNet神经网络的改进策略(L-MobileNet)。将原标准卷积形式替换为深度可分离卷积形式,并将深度卷积层得到的特征图执行取反操作,通过深度卷积融合层传递至下一层;采用Leaky ReLU激活函数代替原ReLU激活函数来保留图像中更多的正负特征信息,并加入类残差结构避免梯度弥散现象。与6种方法进行对比,实验结果表明:L-MobileNet在数据集Cifar-10、Cifar-100(coarse)、Cifar-100(fine)和Dogs vs Cats上平均准确率和最高准确率都取得了最佳结果。
基金supported from The National Philosophy and Social Sciences Foundation(Grant No.20BTQ065).
文摘(Aim)Chinese sign language is an essential tool for hearing-impaired to live,learn and communicate in deaf communities.Moreover,Chinese sign language plays a significant role in speech therapy and rehabilitation.Chinese sign language identification can provide convenience for those hearing impaired people and eliminate the communication barrier between the deaf community and the rest of society.Similar to the research of many biomedical image processing(such as automatic chest radiograph processing,diagnosis of chest radiological images,etc.),with the rapid development of artificial intelligence,especially deep learning technologies and algorithms,sign language image recognition ushered in the spring.This study aims to propose a novel sign language image recognition method based on an optimized convolutional neural network.(Method)Three different combinations of blocks:Conv-BN-ReLU-Pooling,Conv-BN-ReLU,Conv-BN-ReLU-BN were employed,including some advanced technologies such as batch normalization,dropout,and Leaky ReLU.We proposed an optimized convolutional neural network to identify 1320 sign language images,which was called as CNN-CB method.Totally ten runs were implemented with the hold-out randomly set for each run.(Results)The results indicate that our CNN-CB method gained an overall accuracy of 94.88±0.99%.(Conclusion)Our CNN-CB method is superior to thirteen state-of-the-art methods:eight traditional machine learning approaches and five modern convolutional neural network approaches.
文摘眼底血管的健康状态对于研究各类眼科疾病具有重要的参考意义。为了帮助临床医疗人员对眼底微血管形态结构图像的分析来诊断疾病,文中提出了一种基于编码-解码(Encoder-Decoder)结构的U-net的眼底血管分割方法。首先,在模型训练之前对图像进行预处理,然后使用Leaky ReLU激活函数替换U-net ReLU,避免了神经元的死亡问题,同时使用Adam(Adaptive Moment Estimate)优化器代替梯度下降法优化学习策略,最后对血管分割的平均交并比进行计算评估。实验表明,优化后的模型的平均精度可达到93.29%,相比原算法提升了3.26%。
