A heart attack disrupts the normal flow of blood to the heart muscle,potentially causing severe damage or death if not treated promptly.It can lead to long-term health complications,reduce quality of life,and signific...A heart attack disrupts the normal flow of blood to the heart muscle,potentially causing severe damage or death if not treated promptly.It can lead to long-term health complications,reduce quality of life,and significantly impact daily activities and overall well-being.Despite the growing popularity of deep learning,several drawbacks persist,such as complexity and the limitation of single-model learning.In this paper,we introduce a residual learning-based feature fusion technique to achieve high accuracy in differentiating abnormal cardiac rhythms heart sound.Combining MobileNet with DenseNet201 for feature fusion leverages MobileNet lightweight,efficient architecture with DenseNet201,dense connections,resulting in enhanced feature extraction and improved model performance with reduced computational cost.To further enhance the fusion,we employed residual learning to optimize the hierarchical features of heart abnormal sounds during training.The experimental results demonstrate that the proposed fusion method achieved an accuracy of 95.67%on the benchmark PhysioNet-2016 Spectrogram dataset.To further validate the performance,we applied it to the BreakHis dataset with a magnification level of 100X.The results indicate that the model maintains robust performance on the second dataset,achieving an accuracy of 96.55%.it highlights its consistent performance,making it a suitable for various applications.展开更多
为了解指纹图谱技术研究现状并明确其在固废领域的应用现状与前景,依托Web of Science(WOS)核心数据库对2010~2024年相关文献进行检索和分析.发文量分析得知指纹图谱技术依然保持着较高的研究热度,学科聚类分析得知其应用广泛并且近几...为了解指纹图谱技术研究现状并明确其在固废领域的应用现状与前景,依托Web of Science(WOS)核心数据库对2010~2024年相关文献进行检索和分析.发文量分析得知指纹图谱技术依然保持着较高的研究热度,学科聚类分析得知其应用广泛并且近几年在环境领域研究热度也较高;关键词聚类发现研究热点集中在4个方面:Recognition(识别)、ChemoInformatics(化学信息学)、Deep Learning(深度学习)以及Model(模型).对固废领域文献进行关键词共现,得知其关于使用指纹图谱技术进行固废管理的研究较少,主要集中在后处理阶段.进行文献调研,对指纹图谱的数据获取技术、特征提取技术、特征呈现形式进行总结,提出固废领域应用新兴技术的可能性以及未来发展展望.展开更多
文摘A heart attack disrupts the normal flow of blood to the heart muscle,potentially causing severe damage or death if not treated promptly.It can lead to long-term health complications,reduce quality of life,and significantly impact daily activities and overall well-being.Despite the growing popularity of deep learning,several drawbacks persist,such as complexity and the limitation of single-model learning.In this paper,we introduce a residual learning-based feature fusion technique to achieve high accuracy in differentiating abnormal cardiac rhythms heart sound.Combining MobileNet with DenseNet201 for feature fusion leverages MobileNet lightweight,efficient architecture with DenseNet201,dense connections,resulting in enhanced feature extraction and improved model performance with reduced computational cost.To further enhance the fusion,we employed residual learning to optimize the hierarchical features of heart abnormal sounds during training.The experimental results demonstrate that the proposed fusion method achieved an accuracy of 95.67%on the benchmark PhysioNet-2016 Spectrogram dataset.To further validate the performance,we applied it to the BreakHis dataset with a magnification level of 100X.The results indicate that the model maintains robust performance on the second dataset,achieving an accuracy of 96.55%.it highlights its consistent performance,making it a suitable for various applications.
文摘为了解指纹图谱技术研究现状并明确其在固废领域的应用现状与前景,依托Web of Science(WOS)核心数据库对2010~2024年相关文献进行检索和分析.发文量分析得知指纹图谱技术依然保持着较高的研究热度,学科聚类分析得知其应用广泛并且近几年在环境领域研究热度也较高;关键词聚类发现研究热点集中在4个方面:Recognition(识别)、ChemoInformatics(化学信息学)、Deep Learning(深度学习)以及Model(模型).对固废领域文献进行关键词共现,得知其关于使用指纹图谱技术进行固废管理的研究较少,主要集中在后处理阶段.进行文献调研,对指纹图谱的数据获取技术、特征提取技术、特征呈现形式进行总结,提出固废领域应用新兴技术的可能性以及未来发展展望.
