In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these speci...In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these specialists, which impedes the accurate identification and analysis of tumors. This shortage exacerbates the challenge of delivering precise and timely diagnoses and delays the production of comprehensive MRI reports. Such delays can critically affect treatment outcomes, especially for conditions requiring immediate intervention, potentially leading to higher mortality rates. In this study, we introduced an adapted convolutional neural network designed to automate brain tumor diagnosis. Our model features fewer layers, each optimized with carefully selected hyperparameters. As a result, it significantly reduced both execution time and memory usage compared to other models. Specifically, its execution time was 10 times shorter than that of the referenced models, and its memory consumption was 3 times lower than that of ResNet. In terms of accuracy, our model outperformed all other architectures presented in the study, except for ResNet, which showed similar performance with an accuracy of around 90%.展开更多
针对微表情运动的局限性和识别效果不理想的问题,提出了一种结合双注意力模块和ShuffleNet模型的微表情识别方法。该方法将提取的峰值帧的水平和垂直光流图,以通道叠加的方式连接送进所设计的网络进行训练。利用高效且轻量化的ShuffleNe...针对微表情运动的局限性和识别效果不理想的问题,提出了一种结合双注意力模块和ShuffleNet模型的微表情识别方法。该方法将提取的峰值帧的水平和垂直光流图,以通道叠加的方式连接送进所设计的网络进行训练。利用高效且轻量化的ShuffleNet模型堆叠的卷积神经网络(Convolutional neural network,CNN),极大地降低了训练的参数量,在ShuffleNet网络中加入可自适应特征细化的双注意力模块,使得网络在通道和空间维度寻找微表情运动的有用特征信息。在通道注意力模块中,使用一维卷积融合全局池化后的一维通道特征来保持相邻通道的相关性;在空间注意力模块中,采用较小的3×3和5×5卷积核提取不同的空间信息并融合。实验结果表明,在微表情识别方面,相比于基准方法的三个正交平面的局部二值模式(Local binary patterns from three orthogonal planes,LBP-TOP),未加权F1值(Unweighted F1-score,UF1)和未加权平均召回率(Unweighted average recall,UAR)分别提高了0.1445和0.1556,识别性能有很大的提升。展开更多
应用深度学习的图像分析技术,可较早地、无损地检测作物病害,但移动端计算资源的有限性限制了深度学习在移动端的应用和发展。利用迁移学习方法,进行多种神经网络的预训练,将其在ImageNet图像数据集上学到的知识迁移运用到多种农作物数...应用深度学习的图像分析技术,可较早地、无损地检测作物病害,但移动端计算资源的有限性限制了深度学习在移动端的应用和发展。利用迁移学习方法,进行多种神经网络的预训练,将其在ImageNet图像数据集上学到的知识迁移运用到多种农作物数据集及番茄单作物数据集的多种病害识别上,并进行多个深度学习模型在多种作物数据集的计算复杂度、识别效果及计算速度的对比。通过对比发现:Xception模型的计算准确率比较高,计算复杂度稍复杂些;当应用场景对计算准确率的要求不是很高的情况下,ShuffleNet V20.5x模型在计算复杂程度、计算速度的综合表现相对较好,比较适合在移动端进行移植;接着,通过对ShuffleNet V20.5x采用ReLU和LeakyReLU激活函数进行训练和验证分析,发现当采用LeakyReLU激活函数替代原有的ReLU激活函数构建Shuffle Net V20.5x模型,可以改进Shuffle Net V20.5x模型,并能稍微提高识别的准确率,由85.6%提高到86.5%。最后将改进后的ShuffleNet V20.5x模型,移植到移动终端并进行测试。展开更多
文摘In medical imaging, particularly for analyzing brain tumor MRIs, the expertise of skilled neurosurgeons or radiologists is often essential. However, many developing countries face a significant shortage of these specialists, which impedes the accurate identification and analysis of tumors. This shortage exacerbates the challenge of delivering precise and timely diagnoses and delays the production of comprehensive MRI reports. Such delays can critically affect treatment outcomes, especially for conditions requiring immediate intervention, potentially leading to higher mortality rates. In this study, we introduced an adapted convolutional neural network designed to automate brain tumor diagnosis. Our model features fewer layers, each optimized with carefully selected hyperparameters. As a result, it significantly reduced both execution time and memory usage compared to other models. Specifically, its execution time was 10 times shorter than that of the referenced models, and its memory consumption was 3 times lower than that of ResNet. In terms of accuracy, our model outperformed all other architectures presented in the study, except for ResNet, which showed similar performance with an accuracy of around 90%.
文摘针对微表情运动的局限性和识别效果不理想的问题,提出了一种结合双注意力模块和ShuffleNet模型的微表情识别方法。该方法将提取的峰值帧的水平和垂直光流图,以通道叠加的方式连接送进所设计的网络进行训练。利用高效且轻量化的ShuffleNet模型堆叠的卷积神经网络(Convolutional neural network,CNN),极大地降低了训练的参数量,在ShuffleNet网络中加入可自适应特征细化的双注意力模块,使得网络在通道和空间维度寻找微表情运动的有用特征信息。在通道注意力模块中,使用一维卷积融合全局池化后的一维通道特征来保持相邻通道的相关性;在空间注意力模块中,采用较小的3×3和5×5卷积核提取不同的空间信息并融合。实验结果表明,在微表情识别方面,相比于基准方法的三个正交平面的局部二值模式(Local binary patterns from three orthogonal planes,LBP-TOP),未加权F1值(Unweighted F1-score,UF1)和未加权平均召回率(Unweighted average recall,UAR)分别提高了0.1445和0.1556,识别性能有很大的提升。
文摘应用深度学习的图像分析技术,可较早地、无损地检测作物病害,但移动端计算资源的有限性限制了深度学习在移动端的应用和发展。利用迁移学习方法,进行多种神经网络的预训练,将其在ImageNet图像数据集上学到的知识迁移运用到多种农作物数据集及番茄单作物数据集的多种病害识别上,并进行多个深度学习模型在多种作物数据集的计算复杂度、识别效果及计算速度的对比。通过对比发现:Xception模型的计算准确率比较高,计算复杂度稍复杂些;当应用场景对计算准确率的要求不是很高的情况下,ShuffleNet V20.5x模型在计算复杂程度、计算速度的综合表现相对较好,比较适合在移动端进行移植;接着,通过对ShuffleNet V20.5x采用ReLU和LeakyReLU激活函数进行训练和验证分析,发现当采用LeakyReLU激活函数替代原有的ReLU激活函数构建Shuffle Net V20.5x模型,可以改进Shuffle Net V20.5x模型,并能稍微提高识别的准确率,由85.6%提高到86.5%。最后将改进后的ShuffleNet V20.5x模型,移植到移动终端并进行测试。
