生物医学工程学杂志最新文献_第8页

[Construction of a prediction model for induction of labor based on a small sample of clinical indicator data]. [基于小样本临床指标数据构建引产预测模型]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202403033

Yali Qin, Liping Yao, Ling Yuan, Sheng Chen

{"title":"[Construction of a prediction model for induction of labor based on a small sample of clinical indicator data].","authors":"Yali Qin, Liping Yao, Ling Yuan, Sheng Chen","doi":"10.7507/1001-5515.202403033","DOIUrl":"10.7507/1001-5515.202403033","url":null,"abstract":"Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model's generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"1012-1018"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Heart sound classification algorithm based on bispectral feature extraction and convolutional neural networks]. [基于双谱特征提取和卷积神经网络的心音分类算法]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202310016

Liyong Peng, Haiyan Quan

{"title":"[Heart sound classification algorithm based on bispectral feature extraction and convolutional neural networks].","authors":"Liyong Peng, Haiyan Quan","doi":"10.7507/1001-5515.202310016","DOIUrl":"10.7507/1001-5515.202310016","url":null,"abstract":"Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"977-985"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Simulation study on parameter optimization of transcranial direct current stimulation based on rat brain slices]. [基于大鼠脑片的经颅直流电刺激参数优化模拟研究]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202402007

Shiji He, Guanghao Zhang, Changzhe Wu, Xiaolin Huo, Lijun Zhang, Jingxi Zhang, Cheng Zhang

{"title":"[Simulation study on parameter optimization of transcranial direct current stimulation based on rat brain slices].","authors":"Shiji He, Guanghao Zhang, Changzhe Wu, Xiaolin Huo, Lijun Zhang, Jingxi Zhang, Cheng Zhang","doi":"10.7507/1001-5515.202402007","DOIUrl":"10.7507/1001-5515.202402007","url":null,"abstract":"Transcranial direct current stimulation (tDCS) is an important method for treating mental illnesses and neurodegenerative diseases. This paper reconstructed two ex vivo brain slice models based on rat brain slice staining images and magnetic resonance imaging (MRI) data respectively, and the current densities of hippocampus after cortical tDCS were obtained through finite element calculation. Subsequently, a neuron model was used to calculate the response of rat hippocampal pyramidal neuron under these current densities, and the neuronal responses of the two models under different stimulation parameters were compared. The results show that a minimum stimulation voltage of 17 V can excite hippocampal pyramidal neuron in the model based on brain slice staining images, while 24 V is required in the MRI-based model. The results indicate that the model based on brain slice staining images has advantages in precision and electric field propagation simulation, and its results are closer to real measurements, which can provide guidance for the selection of tDCS parameters and scientific basis for precise stimulation.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"945-950"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing]. [通过线性预处理提高体声听诊的自适应降噪性能]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202307058

Hongqiang Mo, Xiang Tian, Bin Li, Junzhang Tian

{"title":"[Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing].","authors":"Hongqiang Mo, Xiang Tian, Bin Li, Junzhang Tian","doi":"10.7507/1001-5515.202307058","DOIUrl":"10.7507/1001-5515.202307058","url":null,"abstract":"Adaptive filtering methods based on least-mean-square (LMS) error criterion have been commonly used in auscultation to reduce ambient noise. For non-Gaussian signals containing pulse components, such methods are prone to weights misalignment. Unlike the commonly used variable step-size methods, this paper introduced linear preprocessing to address this issue. The role of linear preprocessing in improving the denoising performance of the normalized least-mean-square (NLMS) adaptive filtering algorithm was analyzed. It was shown that, the steady-state mean square weight deviation of the NLMS adaptive filter was proportional to the variance of the body sounds and inversely proportional to the variance of the ambient noise signals in the secondary channel. Preprocessing with properly set parameters could suppress the spikes of body sounds, and decrease the variance and the power spectral density of the body sounds, without significantly reducing or even with increasing the variance and the power spectral density of the ambient noise signals in the secondary channel. As a result, the preprocessing could reduce weights misalignment, and correspondingly, significantly improve the performance of ambient-noise reduction. Finally, a case of heart-sound auscultation was given to demonstrate how to design the preprocessing and how the preprocessing improved the ambient-noise reduction performance. The results can guide the design of adaptive denoising algorithms for body sound auscultation.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"969-976"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information]. [基于多分辨率特征融合和上下文信息的胃癌复发预测]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202403014

Hongyu Zhou, Haibo Tao, Feiyue Xue, Bin Wang, Huaiping Jin, Zhenhui Li

{"title":"[Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information].","authors":"Hongyu Zhou, Haibo Tao, Feiyue Xue, Bin Wang, Huaiping Jin, Zhenhui Li","doi":"10.7507/1001-5515.202403014","DOIUrl":"10.7507/1001-5515.202403014","url":null,"abstract":"Pathological images of gastric cancer serve as the gold standard for diagnosing this malignancy. However, the recurrence prediction task often encounters challenges such as insignificant morphological features of the lesions, insufficient fusion of multi-resolution features, and inability to leverage contextual information effectively. To address these issues, a three-stage recurrence prediction method based on pathological images of gastric cancer is proposed. In the first stage, the self-supervised learning framework SimCLR was adopted to train low-resolution patch images, aiming to diminish the interdependence among diverse tissue images and yield decoupled enhanced features. In the second stage, the obtained low-resolution enhanced features were fused with the corresponding high-resolution unenhanced features to achieve feature complementation across multiple resolutions. In the third stage, to address the position encoding difficulty caused by the large difference in the number of patch images, we performed position encoding based on multi-scale local neighborhoods and employed self-attention mechanism to obtain features with contextual information. The resulting contextual features were further combined with the local features extracted by the convolutional neural network. The evaluation results on clinically collected data showed that, compared with the best performance of traditional methods, the proposed network provided the best accuracy and area under curve (AUC), which were improved by 7.63% and 4.51%, respectively. These results have effectively validated the usefulness of this method in predicting gastric cancer recurrence.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"886-894"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning]. [基于机器学习的体内电子顺磁共振波谱分类和辐射剂量预测研究]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202302015

Guangwei Xiong, Bo Chen, Lei Ma, Longpeng Jia, Shunian Chen, Ke Wu, Jing Ning, Bin Zhu, Junwang Guo

{"title":"[Research on in-vivo electron paramagnetic resonance spectrum classification and radiation dose prediction based on machine learning].","authors":"Guangwei Xiong, Bo Chen, Lei Ma, Longpeng Jia, Shunian Chen, Ke Wu, Jing Ning, Bin Zhu, Junwang Guo","doi":"10.7507/1001-5515.202302015","DOIUrl":"10.7507/1001-5515.202302015","url":null,"abstract":"The in-vivo electron paramagnetic resonance (EPR) method can be used for on-site, rapid, and non-invasive detection of radiation dose to casualties after nuclear and radiation emergencies. For in-vivo EPR spectrum analysis, manual labeling of peaks and calculation of signal intensity are often used, which have problems such as large workload and interference by subjective factors. In this study, a method for automatic classification and identification of in-vivo EPR spectra was established using support vector machine (SVM) technology, which can in-batch and automatically identify and screen out invalid spectra due to vibration and dental surface water interference during in-vivo EPR measurements. In this study, a spectrum analysis method based on genetic algorithm optimization neural network (GA-BPNN) was established, which can automatically identify the radiation-induced signals in in-vivo EPR spectra and predict the radiation doses received by the injured. The experimental results showed that the SVM and GA-BPNN spectrum processing methods established in this study could effectively accomplish the automatic spectra classification and radiation dose prediction, and could meet the needs of dose assessment in nuclear emergency. This study explored the application of machine learning methods in EPR spectrum processing, improved the intelligence level of EPR spectrum processing, and would help to enhance the efficiency of mass EPR spectra processing.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"995-1002"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Small-scale cross-layer fusion network for classification of diabetic retinopathy]. [用于糖尿病视网膜病变分类的小型交叉层融合网络]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202403016

Ying Guo, Shaojie Li

{"title":"[Small-scale cross-layer fusion network for classification of diabetic retinopathy].","authors":"Ying Guo, Shaojie Li","doi":"10.7507/1001-5515.202403016","DOIUrl":"10.7507/1001-5515.202403016","url":null,"abstract":"Deep learning-based automatic classification of diabetic retinopathy (DR) helps to enhance the accuracy and efficiency of auxiliary diagnosis. This paper presents an improved residual network model for classifying DR into five different severity levels. First, the convolution in the first layer of the residual network was replaced with three smaller convolutions to reduce the computational load of the network. Second, to address the issue of inaccurate classification due to minimal differences between different severity levels, a mixed attention mechanism was introduced to make the model focus more on the crucial features of the lesions. Finally, to better extract the morphological features of the lesions in DR images, cross-layer fusion convolutions were used instead of the conventional residual structure. To validate the effectiveness of the improved model, it was applied to the Kaggle Blindness Detection competition dataset APTOS2019. The experimental results demonstrated that the proposed model achieved a classification accuracy of 97.75% and a Kappa value of 0.971 7 for the five DR severity levels. Compared to some existing models, this approach shows significant advantages in classification accuracy and performance.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"861-868"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A review on depth perception techniques in organoid images]. [类器官图像深度感知技术综述]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202404036

Yu Sun, Fengliang Huang, Hanwen Zhang, Hao Jiang, Gangyin Luo

{"title":"[A review on depth perception techniques in organoid images].","authors":"Yu Sun, Fengliang Huang, Hanwen Zhang, Hao Jiang, Gangyin Luo","doi":"10.7507/1001-5515.202404036","DOIUrl":"10.7507/1001-5515.202404036","url":null,"abstract":"Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"1053-1061"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[Comparative analysis of the impact of repetitive transcranial magnetic stimulation and burst transcranial magnetic stimulation at different frequencies on memory function and neuronal excitability of mice]. [不同频率的重复经颅磁刺激和脉冲经颅磁刺激对小鼠记忆功能和神经元兴奋性影响的比较分析]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202312017

Rui Fu, Haijun Zhu, Chong Ding, Guizhi Xu

{"title":"[Comparative analysis of the impact of repetitive transcranial magnetic stimulation and burst transcranial magnetic stimulation at different frequencies on memory function and neuronal excitability of mice].","authors":"Rui Fu, Haijun Zhu, Chong Ding, Guizhi Xu","doi":"10.7507/1001-5515.202312017","DOIUrl":"10.7507/1001-5515.202312017","url":null,"abstract":"Transcranial magnetic stimulation (TMS) as a non-invasive neuroregulatory technique has been applied in the clinical treatment of neurological and psychiatric diseases. However, the stimulation effects and neural regulatory mechanisms of TMS with different frequencies and modes are not yet clear. This article explores the effects of different frequency repetitive transcranial magnetic stimulation (rTMS) and burst transcranial magnetic stimulation (bTMS) on memory function and neuronal excitability in mice from the perspective of neuroelectrophysiology. In this experiment, 42 Kunming mice aged 8 weeks were randomly divided into pseudo stimulation group and stimulation groups. The stimulation group included rTMS stimulation groups with different frequencies (1, 5, 10 Hz), and bTMS stimulation groups with different frequencies (1, 5, 10 Hz). Among them, the stimulation group received continuous stimulation for 14 days. After the stimulation, the mice underwent new object recognition and platform jumping experiment to test their memory ability. Subsequently, brain slice patch clamp experiment was conducted to analyze the excitability of granulosa cells in the dentate gyrus (DG) of mice. The results showed that compared with the pseudo stimulation group, high-frequency (5, 10 Hz) rTMS and bTMS could improve the memory ability and neuronal excitability of mice, while low-frequency (1 Hz) rTMS and bTMS have no significant effect. For the two stimulation modes at the same frequency, their effects on memory function and neuronal excitability of mice have no significant difference. The results of this study suggest that high-frequency TMS can improve memory function in mice by increasing the excitability of hippocampal DG granule neurons. This article provides experimental and theoretical basis for the mechanism research and clinical application of TMS in improving cognitive function.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"935-944"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

[A lightweight convolutional neural network for myositis classification from muscle ultrasound images]. [用于肌肉超声图像肌炎分类的轻量级卷积神经网络]。

生物医学工程学杂志 Pub Date : 2024-10-25 DOI: 10.7507/1001-5515.202301023

Hao Tan, Xun Lang, Tao Wang, Bingbing He, Zhiyao Li, Yu Lu, Yufeng Zhang

{"title":"[A lightweight convolutional neural network for myositis classification from muscle ultrasound images].","authors":"Hao Tan, Xun Lang, Tao Wang, Bingbing He, Zhiyao Li, Yu Lu, Yufeng Zhang","doi":"10.7507/1001-5515.202301023","DOIUrl":"10.7507/1001-5515.202301023","url":null,"abstract":"Existing classification methods for myositis ultrasound images have problems of poor classification performance or high computational cost. Motivated by this difficulty, a lightweight neural network based on a soft threshold attention mechanism is proposed to cater for a better IIMs classification. The proposed network was constructed by alternately using depthwise separable convolution (DSC) and conventional convolution (CConv). Moreover, a soft threshold attention mechanism was leveraged to enhance the extraction capabilities of key features. Compared with the current dual-branch feature fusion myositis classification network with the highest classification accuracy, the classification accuracy of the network proposed in this paper increased by 5.9%, reaching 96.1%, and its computational complexity was only 0.25% of the existing method. The obtained results support that the proposed method can provide physicians with more accurate classification results at a lower computational cost, thereby greatly assisting them in their clinical diagnosis.","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"41 5","pages":"895-902"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11527749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0