BMC Infectious Diseases (2024) 24:1181 https://doi.org/10.1186/s12879-024-10032-9 BMC Infectious Diseases RESEARCH Open Access Prediction of lumpy skin disease virus using customized CBAM‑DenseNet‑attention model Muhammad Mujahid1†, Tahir Khurshaid2†, Mejdl Safran3, Sultan ...
On the ground of original DenseNet model, the method in this paper adds CBAM to each Dense Block of the model, extracts the image features with regional attention and channel attention, and adopts the triple loss function to achieve the retrieve of remote sensing images. The experiment results ...
The models that performed best in the breast tumor segmentation task are clearly shown by the performance results, especially when considering the Dice score—a crucial metric for assessing segmentation overlap. With an overall Dice score of 0.6140, Model 1, which combines BCE Loss with CBAM Attent...
On the ground of original DenseNet model, the method in this paper adds CBAM to each Dense Block of the model, extracts the image features with regional attention and channel attention, and adopts the triple loss function to achieve the retrieve of remote sensing images. The experiment results ...
上图为pytorch实现的densenet-121中的transition layer的网络结构。 稠密链接 稠密链接是在一个dense block中执行,每个dense block含有若干个dense layer(bottleneck layer)。上图为一个block中不同feature maps的稠密链接方式,即一个某feature maps经过一个bottleneck layer后,输出k维的feature maps,然后和前面所有的feat...
对于CBAM 我们有两种添加方式,一种是在交换不同路径特征图中使用注意力来学习需要交换的信息,另一种则是对相同路径的特征图使用注意力来学习需要传递的信息,经过多次实验发现第二种方式更有效且更利于推广到一般网络。 二、项目标准与技术指标 2.1 项目预期应用 首先,针对婴幼儿期的脑磁影像的分割任务是研究早期大脑...
However, a crucial challenge lies in optimizing computational efficiency and reducing training time while maintaining high accuracy with limited hardware resources. This paper introduces ResdenseNet, a model built upon the MobileNet, DenseNet, and ResNet architectures. ResdenseNet combines dense blocks and...
首先，该模型利用三维卷积核同步提取联合光谱空间特征，同时密集连接网络(DenseNet)的稠密连接块除了能够充分提取更加鲁棒的特征外，还减少了大量参数；其次，自注意力(self-attention)机制作为一个模块加入到稠密连接块中，可以使上层提取到的特征在进入下一层网络之前，经过该模块对其进行权重分配，使具有丰富的物类别信息...
可视化分类方法.结合N-gram和TF-IDF技术对恶意代码数据集进行处理,并将其转化为灰度图.随后,引入CBAM并调整密集块数量,构建DenseNet88_CBAM网络模型用于灰度图分类... 王金伟,陈正嘉,谢雪,... - 《通信学报》 被引量: 0发表: 2024年 Deep visualization classification method for malicious code based on Ngram...
There are two pathways in ATP-DenseNet: Feature pyramid could extract hierarchical page feature, and attention-based DenseNet (A-DenseNet) could extract the word feature by fusing Convolutional Block Attention Module (CBAM) and dense connected block. Finally, ATP-DenseNet makes the final prediction ...