Abstract：
BACKGROUND: Chest X-ray image classification for multiple diseases is an important research direction in the field of computer vision and medical image processing. It aims to utilize advanced image processing techniques and deep learning algorithms to automatically analyze and identify X-ray images, determining whether specific pathologies or structural abnormalities exist in the images.
OBJECTIVE: We present the MMPDenseNet network designed specifically for chest multi-label disease classification.
METHODS: Initially, the network employs the adaptive activation function Meta-ACON to enhance feature representation. Subsequently, the network incorporates a multi-head self-attention mechanism, merging the conventional convolutional neural network with the Transformer, thereby bolstering the ability to extract both local and global features. Ultimately, the network integrates a pyramid squeeze attention module to capture spatial information and enrich the feature space.
RESULTS: The concluding experiment yielded an average AUC of 0.898, marking an average accuracy improvement of 0.6% over the baseline model. When compared with the original network, the experimental results highlight that MMPDenseNet considerably elevates the classification accuracy of various chest diseases.
CONCLUSIONS: It can be concluded that the network, thus, holds substantial value for clinical applications.
OBJECTIVE: We present the MMPDenseNet network designed specifically for chest multi-label disease classification.
METHODS: Initially, the network employs the adaptive activation function Meta-ACON to enhance feature representation. Subsequently, the network incorporates a multi-head self-attention mechanism, merging the conventional convolutional neural network with the Transformer, thereby bolstering the ability to extract both local and global features. Ultimately, the network integrates a pyramid squeeze attention module to capture spatial information and enrich the feature space.
RESULTS: The concluding experiment yielded an average AUC of 0.898, marking an average accuracy improvement of 0.6% over the baseline model. When compared with the original network, the experimental results highlight that MMPDenseNet considerably elevates the classification accuracy of various chest diseases.
CONCLUSIONS: It can be concluded that the network, thus, holds substantial value for clinical applications.
摘要：
背景：多种疾病的胸部X线图像分类是计算机视觉和医学图像处理领域的重要研究方向。它旨在利用先进的图像处理技术和深度学习算法来自动分析和识别X射线图像，确定图像中是否存在特定的病理或结构异常。
目的：我们提出了专为胸部多标签疾病分类而设计的MMPDenseNet网络。
方法：最初，网络采用自适应激活函数Meta-ACON来增强特征表示。随后，该网络包含多头自我注意机制，将传统的卷积神经网络与Transformer合并，从而增强提取局部和全局特征的能力。最终,该网络集成了金字塔挤压注意力模块，以捕获空间信息并丰富特征空间。
结果：结论实验产生的平均AUC为0.898，与基线模型相比，平均准确度提高了0.6%。与原始网络相比，实验结果表明，MMPDenseNet大大提高了各种胸部疾病的分类精度。
结论：可以得出结论，因此，具有重要的临床应用价值。
目的：我们提出了专为胸部多标签疾病分类而设计的MMPDenseNet网络。
方法：最初，网络采用自适应激活函数Meta-ACON来增强特征表示。随后，该网络包含多头自我注意机制，将传统的卷积神经网络与Transformer合并，从而增强提取局部和全局特征的能力。最终,该网络集成了金字塔挤压注意力模块，以捕获空间信息并丰富特征空间。
结果：结论实验产生的平均AUC为0.898，与基线模型相比，平均准确度提高了0.6%。与原始网络相比，实验结果表明，MMPDenseNet大大提高了各种胸部疾病的分类精度。
结论：可以得出结论，因此，具有重要的临床应用价值。
