Heart attack prediction is a vital component of cardiovascular healthcare, aiming to identify individuals at risk for timely intervention and improved patient outcomes. Despite significant advancements in predictive modeling techniques, several challenges persist, including algorithmic limitations, interpretability issues, data dependence, and scalability concerns. These challenges underscore the need for robust, interpretable, and generalizable predictive models capable of handling the complexities of medical data effectively. In this study, we propose a novel approach leveraging the eXtreme Gradient Boosting (XGBoost) algorithm for heart attack analysis and prediction. We conducted a comprehensive analysis of heart disease datasets, employing rigorous data preprocessing, feature selection, and hyperparameter optimization techniques to develop a highly accurate and interpretable predictive model. Our results demonstrate the efficacy of the XGBoost algorithm in capturing intricate patterns from medical data, achieving superior predictive performance across various metrics. The proposed model addresses the existing challenges in heart attack prediction, offering a promising solution for enhancing cardiovascular healthcare outcomes.
{"title":"Enhanced Heart Attack Prediction Using eXtreme Gradient Boosting","authors":"Mingyang Feng, Xiaosong Wang, Zhiming Zhao, Chufeng Jiang, Jize Xiong, Ning Zhang","doi":"10.53469/jtpes.2024.04(04).02","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(04).02","url":null,"abstract":"Heart attack prediction is a vital component of cardiovascular healthcare, aiming to identify individuals at risk for timely intervention and improved patient outcomes. Despite significant advancements in predictive modeling techniques, several challenges persist, including algorithmic limitations, interpretability issues, data dependence, and scalability concerns. These challenges underscore the need for robust, interpretable, and generalizable predictive models capable of handling the complexities of medical data effectively. In this study, we propose a novel approach leveraging the eXtreme Gradient Boosting (XGBoost) algorithm for heart attack analysis and prediction. We conducted a comprehensive analysis of heart disease datasets, employing rigorous data preprocessing, feature selection, and hyperparameter optimization techniques to develop a highly accurate and interpretable predictive model. Our results demonstrate the efficacy of the XGBoost algorithm in capturing intricate patterns from medical data, achieving superior predictive performance across various metrics. The proposed model addresses the existing challenges in heart attack prediction, offering a promising solution for enhancing cardiovascular healthcare outcomes.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.53469/jtpes.2024.04(04).03
Zhihe Tang, Zewei Liu
Pulse shaping can greatly improve the signal-to-noise ratio and has been widely used in nuclear signal processing. This paper gives a brief overview of the current research status of nuclear signal pulse shaping at home and abroad. It describes in detail the three traditional pulse shaping methods of quasi-Gaussian shaping, triangular shaping and trapezoidal shaping, and discusses the nuclear pulse signal shaping that has appeared in recent years. And a brief introduction to the nuclear pulse signal shaping methods that have emerged in recent years is also given.
{"title":"Review of Research on Nuclear Signal Pulse Shaping","authors":"Zhihe Tang, Zewei Liu","doi":"10.53469/jtpes.2024.04(04).03","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(04).03","url":null,"abstract":"Pulse shaping can greatly improve the signal-to-noise ratio and has been widely used in nuclear signal processing. This paper gives a brief overview of the current research status of nuclear signal pulse shaping at home and abroad. It describes in detail the three traditional pulse shaping methods of quasi-Gaussian shaping, triangular shaping and trapezoidal shaping, and discusses the nuclear pulse signal shaping that has appeared in recent years. And a brief introduction to the nuclear pulse signal shaping methods that have emerged in recent years is also given.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"32 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.53469/jtpes.2024.04(04).06
Hongjie Ji, Hongbo Ji
With the continuous improvement of our country's economic strength, the level of industrial technology is also continuous progress, our country began the development of mechanical automation. In this paper, the mechanical automation control system is summarized, to understand its definition and working principle, the characteristics of mechanization automatic technology analysis, clear mechanical automation control system development points.
{"title":"A Review on Mechanical Automation Control System","authors":"Hongjie Ji, Hongbo Ji","doi":"10.53469/jtpes.2024.04(04).06","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(04).06","url":null,"abstract":"With the continuous improvement of our country's economic strength, the level of industrial technology is also continuous progress, our country began the development of mechanical automation. In this paper, the mechanical automation control system is summarized, to understand its definition and working principle, the characteristics of mechanization automatic technology analysis, clear mechanical automation control system development points.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140653893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.53469/jtpes.2024.04(04).04
Yang Chen, Shaojie Wang
With the increase of service life of masonry structure and the damage of masonry structure caused by external environment, the structure will be difficult to meet the design requirements. It is necessary to repair and reinforce the existing structure or replace the original structure with a new type of composite structure. ultra-high performance concrete (UHPC) is a kind of high strength, high ductility, high durability material, which has the advantages of almost impermeability, almost no carbonization, and almost zero chloride ion penetration and sulfate penetration. UHPC has been widely used in the field of concrete structure reinforcement, whether the masonry structure can be better strengthened has become the research content of this paper. Basic mechanics of UHPC materials at home and abroad By systematically combing and summarizing the research progress of performance and reinforcement design, the feasibility of UHPC reinforced masonry structure is obtained.
{"title":"Feasibility Study of UHPC Reinforced Masonry Structure","authors":"Yang Chen, Shaojie Wang","doi":"10.53469/jtpes.2024.04(04).04","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(04).04","url":null,"abstract":"With the increase of service life of masonry structure and the damage of masonry structure caused by external environment, the structure will be difficult to meet the design requirements. It is necessary to repair and reinforce the existing structure or replace the original structure with a new type of composite structure. ultra-high performance concrete (UHPC) is a kind of high strength, high ductility, high durability material, which has the advantages of almost impermeability, almost no carbonization, and almost zero chloride ion penetration and sulfate penetration. UHPC has been widely used in the field of concrete structure reinforcement, whether the masonry structure can be better strengthened has become the research content of this paper. Basic mechanics of UHPC materials at home and abroad By systematically combing and summarizing the research progress of performance and reinforcement design, the feasibility of UHPC reinforced masonry structure is obtained.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"56 29","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140656589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.53469/jtpes.2024.04(04).05
Bo Fu
New era of rapid development and progress, to promote science and technology is increasingly perfect, manufacturing to meet customer demand, puts forward higher requirements on mechanical precision mold processing, and mechanical processing plant to ensure the precision mold processing, must improve the level of staff technology practice and professional quality, and constantly learning new technology, new equipment, and set up correct GongZuoGuan, Clearly understand the relationship between the machining accuracy of mechanical die and its application, deeply analyze its influencing factors, and put forward targeted control measures. Based on this, this paper carries out an in-depth analysis of the machining precision control of mechanical die, for reference only.
{"title":"Analysis on Machining Precision Control of Mechanical Die","authors":"Bo Fu","doi":"10.53469/jtpes.2024.04(04).05","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(04).05","url":null,"abstract":"New era of rapid development and progress, to promote science and technology is increasingly perfect, manufacturing to meet customer demand, puts forward higher requirements on mechanical precision mold processing, and mechanical processing plant to ensure the precision mold processing, must improve the level of staff technology practice and professional quality, and constantly learning new technology, new equipment, and set up correct GongZuoGuan, Clearly understand the relationship between the machining accuracy of mechanical die and its application, deeply analyze its influencing factors, and put forward targeted control measures. Based on this, this paper carries out an in-depth analysis of the machining precision control of mechanical die, for reference only.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"20 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.53469/jtpes.2024.04(04).07
Hao Liu, Yi Shen, Shuangjiang Yu, Zijun Gao, Tong Wu
Path planning is an important problem with the the applications in many aspects, such as video games, robotics etc. This paper proposes a novel method to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. We design DRL-based algorithms, including reward functions, and parameter optimization, to avoid time-consuming work in a 2D environment. We also designed an Two-way search hybrid A* algorithm to improve the quality of local path planning. We transferred the designed algorithm to a simple embedded environment to test the computational load of the algorithm when running on a mobile robot. Experiments show that when deployed on a robot platform, the DRL-based algorithm in this article can achieve better planning results and consume less computing resources.
{"title":"Deep Reinforcement Learning for Mobile Robot Path Planning","authors":"Hao Liu, Yi Shen, Shuangjiang Yu, Zijun Gao, Tong Wu","doi":"10.53469/jtpes.2024.04(04).07","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(04).07","url":null,"abstract":"Path planning is an important problem with the the applications in many aspects, such as video games, robotics etc. This paper proposes a novel method to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. We design DRL-based algorithms, including reward functions, and parameter optimization, to avoid time-consuming work in a 2D environment. We also designed an Two-way search hybrid A* algorithm to improve the quality of local path planning. We transferred the designed algorithm to a simple embedded environment to test the computational load of the algorithm when running on a mobile robot. Experiments show that when deployed on a robot platform, the DRL-based algorithm in this article can achieve better planning results and consume less computing resources.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"713 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140718912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.53469/jtpes.2024.04(03).21
Ziyue Wang
Financial fraud is a hidden criminal activity that has a serious impact on the stability of financial markets and investor confidence. Traditional fraud detection methods are often inefficient and unable to meet the rapidly changing fraudulent means. The development of artificial intelligence and big data technology has provided new solutions for financial fraud detection. This article aims to explore the current application status and methods of artificial intelligence and big data technology in financial fraud detection, and analyze the challenges and future development directions.
{"title":"Research on the Application of Artificial Intelligence and Big Data Technology in Financial Fraud Detection","authors":"Ziyue Wang","doi":"10.53469/jtpes.2024.04(03).21","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(03).21","url":null,"abstract":"Financial fraud is a hidden criminal activity that has a serious impact on the stability of financial markets and investor confidence. Traditional fraud detection methods are often inefficient and unable to meet the rapidly changing fraudulent means. The development of artificial intelligence and big data technology has provided new solutions for financial fraud detection. This article aims to explore the current application status and methods of artificial intelligence and big data technology in financial fraud detection, and analyze the challenges and future development directions.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"26 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140753489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.53469/jtpes.2024.04(02).03
Ying Xie, Zhengning Li, Yibo Yin, Zibu Wei, Guokun Xu, Yang Luo
The escalating volume and intricacy of legal documents necessitate advanced techniques for automated text classification in the legal domain. Our proposed approach leverages Convolutional Neural Networks (Conv1D), a neural network architecture adept at capturing hierarchical features in sequential data. The incorporation of max-pooling facilitates the extraction of salient features, while softmax activation enables the model to handle the multi-class nature of legal citation categorization. By addressing the limitations identified in previous studies, our model aims to advance the state-of-the-art in legal citation text classification, offering a robust and efficient solution for automated categorization in the legal domain. Our research contributes to the ongoing evolution of NLP applications in the legal field, promising enhanced accuracy and adaptability in the automated analysis of legal texts.
{"title":"Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification","authors":"Ying Xie, Zhengning Li, Yibo Yin, Zibu Wei, Guokun Xu, Yang Luo","doi":"10.53469/jtpes.2024.04(02).03","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).03","url":null,"abstract":"The escalating volume and intricacy of legal documents necessitate advanced techniques for automated text classification in the legal domain. Our proposed approach leverages Convolutional Neural Networks (Conv1D), a neural network architecture adept at capturing hierarchical features in sequential data. The incorporation of max-pooling facilitates the extraction of salient features, while softmax activation enables the model to handle the multi-class nature of legal citation categorization. By addressing the limitations identified in previous studies, our model aims to advance the state-of-the-art in legal citation text classification, offering a robust and efficient solution for automated categorization in the legal domain. Our research contributes to the ongoing evolution of NLP applications in the legal field, promising enhanced accuracy and adaptability in the automated analysis of legal texts.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous development of machine learning technology, the scientific research of biomedical materials is gradually shifting to a data-driven direction. The rise of this trend stems from the widespread use of Bio sequencing technology, which provides entirely new methods and insights for testing and evaluating the biological function of biomedical materials. The performance and performance of biomedical materials have a wide range of applications in medical applications, drug delivery, biosensors and other fields, so it is important to further optimize them. However, with the accumulation and increasing complexity of data, there is a need for more intelligent and efficient ways to process and analyze this heterogeneous scientific data. Therefore, the establishment of an open, shared infrastructure for storing heterogeneous scientific data from different research fields will be the cornerstone of cross-disciplinary joint analysis. This infrastructure will not only accelerate the collection and integration of data, but will also provide opportunities for collaboration and innovation across disciplines. This paper highlights a new trend in biomedical materials research, namely a data-driven approach, and the key role of Bio sequencing technology in this process. At the same time, we call for the establishment of an open data storage and sharing platform to promote multidisciplinary cooperation, accelerate the optimization and innovation of biomedical materials, and open up broader prospects for future biomedical applications. This effort is expected to push scientific research in the medical field to new heights, providing safer and more effective treatments and medical programs for patients.
{"title":"Utilizing AI-Enhanced Multi-Omics Integration for Predictive Modeling of Disease Susceptibility in Functional Phenotypes","authors":"Yanlin Zhou, Xinyu She, Zheng He, Huiying Weng, Wangmei Chen","doi":"10.53469/jtpes.2024.04(02).07","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).07","url":null,"abstract":"With the continuous development of machine learning technology, the scientific research of biomedical materials is gradually shifting to a data-driven direction. The rise of this trend stems from the widespread use of Bio sequencing technology, which provides entirely new methods and insights for testing and evaluating the biological function of biomedical materials. The performance and performance of biomedical materials have a wide range of applications in medical applications, drug delivery, biosensors and other fields, so it is important to further optimize them. However, with the accumulation and increasing complexity of data, there is a need for more intelligent and efficient ways to process and analyze this heterogeneous scientific data. Therefore, the establishment of an open, shared infrastructure for storing heterogeneous scientific data from different research fields will be the cornerstone of cross-disciplinary joint analysis. This infrastructure will not only accelerate the collection and integration of data, but will also provide opportunities for collaboration and innovation across disciplines. This paper highlights a new trend in biomedical materials research, namely a data-driven approach, and the key role of Bio sequencing technology in this process. At the same time, we call for the establishment of an open data storage and sharing platform to promote multidisciplinary cooperation, accelerate the optimization and innovation of biomedical materials, and open up broader prospects for future biomedical applications. This effort is expected to push scientific research in the medical field to new heights, providing safer and more effective treatments and medical programs for patients.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-28DOI: 10.53469/jtpes.2024.04(02).01
Yulu Gong, Houze Liu, Lianwei Li, Jingxiao Tian, Hanzhe Li
In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, image registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised image registration techniques for single-modal imagery, this research introduces an innovative deep learning-based image registration algorithm. Its novelty resides in integrating short and long connections to create a densely connected structure, markedly refining the feature map interconnectivity within the U-Net architecture. This advancement addresses the significant semantic gap issues arising from disparities in feature map sampling depths. Moreover, the algorithm incorporates a channel attention mechanism within the U-shaped network's decoder, significantly mitigating image noise and facilitating the generation of smoother deformation fields. This enhancement not only boosts the model's detail sensitivity but also markedly increases image registration precision, particularly evident when processing single-modal brain MRI datasets, thereby proving the algorithm's efficacy and utility. Extensive clinical application-based training and testing have underscored this algorithm's substantial contributions to medical image registration accuracy enhancement. Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance.
在手术导航和肿瘤监测等关键临床医学图像分析应用中，图像配准至关重要。认识到现有单模态图像无监督图像配准技术在提高准确性方面的潜力，这项研究引入了一种基于深度学习的创新图像配准算法。该算法的新颖之处在于整合了短连接和长连接以创建密集连接结构，明显改善了 U-Net 架构内的特征图互连性。这一进步解决了因特征图采样深度不同而产生的重大语义差距问题。此外，该算法还在 U 型网络的解码器中加入了信道注意机制，大大减轻了图像噪声，有利于生成更平滑的形变场。这一改进不仅提高了模型的细节灵敏度，还显著提高了图像配准精度，这在处理单模态脑磁共振成像数据集时尤为明显，从而证明了该算法的有效性和实用性。基于临床应用的广泛培训和测试凸显了该算法对提高医学图像配准精度的巨大贡献。总之，通过利用深度学习技术和创新算法结构，这项研究解决了医学图像配准中的关键难题，为手术导航和肿瘤监测等临床应用提供了更精确、更可靠的支持。
{"title":"Deep Learning-Based Medical Image Registration Algorithm: Enhancing Accuracy with Dense Connections and Channel Attention Mechanisms","authors":"Yulu Gong, Houze Liu, Lianwei Li, Jingxiao Tian, Hanzhe Li","doi":"10.53469/jtpes.2024.04(02).01","DOIUrl":"https://doi.org/10.53469/jtpes.2024.04(02).01","url":null,"abstract":"In critical clinical medical image analysis applications, such as surgical navigation and tumor monitoring, image registration is crucial. Recognizing the potential for enhanced accuracy in existing unsupervised image registration techniques for single-modal imagery, this research introduces an innovative deep learning-based image registration algorithm. Its novelty resides in integrating short and long connections to create a densely connected structure, markedly refining the feature map interconnectivity within the U-Net architecture. This advancement addresses the significant semantic gap issues arising from disparities in feature map sampling depths. Moreover, the algorithm incorporates a channel attention mechanism within the U-shaped network's decoder, significantly mitigating image noise and facilitating the generation of smoother deformation fields. This enhancement not only boosts the model's detail sensitivity but also markedly increases image registration precision, particularly evident when processing single-modal brain MRI datasets, thereby proving the algorithm's efficacy and utility. Extensive clinical application-based training and testing have underscored this algorithm's substantial contributions to medical image registration accuracy enhancement. Overall, by leveraging deep learning technologies and innovative algorithmic structures, this study addresses pivotal challenges in medical image registration, offering more precise and dependable support for clinical applications like surgical navigation and tumor surveillance.","PeriodicalId":489516,"journal":{"name":"Journal of Theory and Practice of Engineering Science","volume":"578 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140417111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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