Yujuan Si's research while affiliated with University of Electronic Science and Technology of China and other places
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Publications (58)
Accurate classification of congestive heart failure (CHF) is essential to reduce the mortality of cardiovascular disease. Many existing researches suffer from unsatisfactory performance in inter-patient scheme closer to clinical application. To address this issue, this paper presents a novel attention-based multi-scale convolutional neural network...
In recent years, skin cancer has been recognized as the most dangerous and common type of cancer in humans. There are different types of skin cancer that can be diagnosed early. Providing a method that facilitates the diagnosis of this cancer in the early stages is very useful and valuable. This study proposes a new algorithm to efficient diagnosis...
Accurate classification of heartbeats is essential for the treatment of cardiac arrhythmia. In real-world applications, many existing methods suffer from the imbalance between heartbeat classes since the number of normal heartbeats heavily outnumbers that of abnormal heartbeats. To address the class imbalance problem effectively, this study propose...
This paper classifies non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats in the MIT-BIH arrhythmia database. The classification encounters serious class imbalance since the number of beats in N (majority class) with sample number above the average per class is heavily outnumbered than that in S, V, and F (m...
An attack of congestive heart failure (CHF) can cause symptoms such as difficulty breathing, dizziness, or fatigue, which can be life-threatening in severe cases. An electrocardiogram (ECG) is a simple and economical method for diagnosing CHF. Due to the inherent complexity of ECGs and the subtle differences in the ECG waveform, misdiagnosis happen...
In recent years, personal information leakage incidents have occurred frequently. Currently, a safe and efficient identification method is essential to prevent data leakage. The electrocardiogram (ECG) has attracted more and more attention because of its high security. Researchers have made many progresses in the study of ECG identification. Howeve...
This study proposes a lung cancer diagnosis system based on computed tomography (CT) scan images for the detection of the disease. The proposed method uses a sequential approach to achieve this goal. Consequently, two well‐organized classifiers, the convolutional neural network (CNN) and feature‐based methodology, have been used. In the first step,...
This paper presents an active and incremental learning system called active broad learning system (ABLS) for ECG arrhythmia classification to reduce the time-consumption of training and labor cost of experts labeling beats. An effective strategy is designed to convert the actual outputs in broad learning system (BLS) into approximated posterior pro...
In recent years, with the increasing standard of biometric identification, it is difficult to meet the requirements of data size and accuracy in practical application for training a single ECG (electrocardiogram) database. The paper aims to construct a recognition model for processing multi-source data and proposes a novel ECG identification system...
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In recent years, methods based on rank reduction, such as nuclear norm minimization (NNM), have achieved remarkable results in seismic signal processing. These methods are used to threshold the singular values of the degraded signals, so as to estimate the singular values of the clean signals directly. Although the effect is obvious, it is easy to...
Coronary artery disease (CAD) and congestive heart failure (CHF) lead to many deaths worldwide. Generally, an electrocardiogram (ECG) is employed as the diagnostic tool for CAD/CHF recognition. However, since ECG changes are sometimes subtle, visually distinguishing long-term ECG abnormalities is time consuming and laborious. To address these issue...
Attention has been diffusely used in many tasks since it can guide network concentrating on the most important regions of an input pattern. Nevertheless, many advanced works focus on first-order attention design, e.g. channels and spatial attention, but ignore higher-order attention mechanisms. In this work, we propose the Mixed High-Order Attentio...
In this work, we propose a novel multiscale transform decomposition model for multi-focus image fusion to get a better fused performance. The motivation of the proposed fusion framework is to make full use of the decomposition characteristics of multiscale transform. The nonsubsampled contourlet transform (NSCT) is firstly used to decompose the sou...
Feature extraction plays an important role in Electrocardiogram (ECG) Beats classification system. Compared to other popular methods, VQ method performs well in feature extraction from ECG with advantages of dimensionality reduction. In VQ method, a set of dictionaries corresponding to segments of ECG beats is trained, and VQ codes are used to repr...
In order to further improve the contrast and sharpness of fused image, a novel multi-focus image fusion algorithm based on spatial frequency-motivated parameter-adaptive pulse coupled neural network (SF-PAPCNN) and improved sum-modified-laplacian (ISML) in nonsubsampled shearlet transform (NSST) domain is proposed in this paper. In its procedural s...
Cardiovascular disease is the leading cause of death worldwide. Immediate and accurate diagnoses of cardiovascular disease are essential for saving lives. Although most of the previously reported works have tried to classify heartbeats accurately based on the intra-patient paradigm, they suffer from category imbalance issues since abnormal heartbea...
Recently, the Bag-Of-Word (BOW) algorithm provides efficient features and promotes the accuracy of the ECG classification system. However, BOW algorithm has two shortcomings: (1). it has large quantization errors and poor reconstruction performance; (2). it loses heart beat's time information, and may provide confusing features for different kinds...
In this work, a novel image enhancement algorithm using NSST and SVD is proposed to improve the definition of the acquired brain images. The input brain image is computed by CLAHE, then the processed brain image and input brain image are decomposed into low- and high-frequency components by NSST, the singular value matrix of the low-frequency compo...
This paper studies the theoretical problems and implementation methods of stream ciphers, discusses some deficiencies of conventional stream cipher algorithms, designs methods and rules for random Dynamic Hash Mapping and Bits Scrambling, applies nonlinear feedback shift register (NLFSR) technology, and a new stream cipher method with higher securi...
Medical image fusion is a principal category in the medical applications which has great impacts on the final diagnosis results. In this study, a hybrid optimization technique is presented for developing a high efficiency technique for the fusion of the medical images. The presented method uses both advantages of the wavelet transform and the homom...
Coronary artery disease (CAD) and congestive heart failure (CHF) occur worldwide, putting patients at risk of death. Researchers have developed many automatic methods for CAD and CHF classification. However, most have neglected evaluating the performance of these methods in inter-patient experiments that can guarantee their generalization in practi...
The principle of image super-resolution reconstruction (SR) is to pass one or more low-resolution (LR) images through information processing technology to obtain the final high-resolution (HR) image. Convolutional neural networks (CNN) have achieved better results than traditional methods in the process of an image super-resolution reconstruction....
Electrocardiograms (ECGs) have been extensively utilized for diagnosing cardiovascular abnormalities. However, due to the mixed noise and the subtle differences between ECGs, it is generally arduous to spot the ECG abnormalities with satisfactory efficiency with the naked eye. To address these issues, we proposed a novel automatic system for diagno...
Myocardial infarction (MI) is a deadly disease that threatens human life worldwide, and it is essential to save threatened lives with early detection of MI. The electrocardiogram (ECG), which records the electrical activity presented in the heart, is used for the prevention and treatment of heart disease such as MI. However, it remains a challenge...
Cardiovascular disease (CVD) has become one of the most serious diseases that threaten human health. Over the past decades, over 150 million humans have died of CVDs. Hence, timely prediction of CVDs is especially important. Currently, deep learning algorithm-based CVD diagnosis methods are extensively employed, however, most such algorithms can on...
In order to deal with the pseudo-Gibbs phenomenon in the process of hyperspectral remote sensing image enhancement, a novel image enhancement method based on nonsubsampled shearlet transform (NSST) is proposed in this paper. The main motivation of this study is to adjust the coefficient of remote sensing image enhancement as a pattern recognition t...
Electrocardiogram (ECG) is a weak electrical signal that reflects the process of heart activity, and has multiple excellent features such as uniqueness, stability, versatility, non-repeatability, easy collection and so on. As a new type of biometric authentication technology, the feature extraction and classification of ECG have become a hot resear...
In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significant...
Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbe...
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An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Deep convolutional neural networks (CNN) can automatically extract valid features from data, which is an effective way for the classification of the ECG beats. However, the fully-connected layer in CNNs requires a fixed input dimension,...
Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by heartbeat noise and are negatively affected by skewed data. To address these problems, a novel heartbeat recognition method is presented. The aim of thi...
In order to deal with the pseudo-Gibbs phenomenon and noise interference in the image enhancement, a novel remote sensing image enhancement technique based on unsharp masking and non-subsampled shearlet transform (NSST) is proposed in this paper. The steps of the proposed model are described as follows: Firstly, the input image is decomposed into o...
In this paper, a novel microscopy mineral image enhancement method based on adaptive threshold in non-subsampled shearlet transform (NSST) domain is proposed. First, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands. Second, the gamma correction is applied to process the low-frequency sub-band coefficients...
In this article, a novel brain image enhancement approach based on nonsubsampled contourlet transform (NSCT) is proposed. First, the image is decomposed into a low-frequency component and several high-frequency components by the NSCT; Second, the gamma correction is applied to deal with the low-frequency sub-band coefficients, and the adaptive thre...
In this paper, a novel remote sensing image enhancement technique based on a non-local means filter in a nonsubsampled contourlet transform (NSCT) domain is proposed. The overall flow of the approach can be divided into the following steps: Firstly, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands with NS...
With the quick development of information technology, people pay more and more attention to information security and property safety, where identity is one of the most important aspects of information security. Compared with the traditional means of identification, biometrics recognition technology offers greater security and convenience. Among whi...
The ECG parameters extraction and ECG identification serve as the foundation for ECG Automatic Identification, which has become the hot issue in the field of signal processing [1]. This paper makes a study on the existing QRS complexes detection algorithms, and makes an analysis of the characteristics of differential operator. Based on the real-tim...
Citations
... FOSMA [133] Fractional Calculus-Based Slime Mould Algorithm Feature selection FOS-CS [134] Fractional-order Cuckoo Search Optimizer COVID-19 X-ray images classification FOCLMPA [135] Fractional-order marine predators algorithm Feature selection FC-FPA [136] Fractional chaos flower pollination algorithm Feature selection FO-MPA [137] fractional-order marine predators algorithm COVID-19 image classification FO-COA [138] Fractional Order Coot Optimization Algorithm Skin cancer detection FO-CRO [139] Fractional-order Chaotic Chemical Reaction Optimization Parameter identification ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/1007909316075521-1617315715844_Q64/Dmitry-Bokov.jpg)
... The BLS is effective and has shown brilliant results in diverse classification and pattern recognition studies. Many researchers have developed and implemented this model and made many meaningful achievements [8][9][10][11][12][13]. For instance, Feng et al. implemented and integrated a fuzzy system into the basic BLS to replace original feature nodes with a group of Takagi-Sugeno fuzzy subsystems [14]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/913832218218500-1594885987772_Q64/Weiyi-Yang-7.jpg)
... At present, the BLS has been widely used in the areas of data classification and state prediction. [42][43][44] However, when there are too much sequence number of training samples or too many feature/enhancement nodes, searching the global optimal output weights by ridge regression requires to solve the inverse of high-latitude matrix and store intermediate results, reducing the computational efficiency to a large extent. ...
... As shown in Table 4, the proposed method obtained the best performance compared to previous studies of five Classifications [40][41][42]. Liu et al. proposed ECVT-Net [43] has better performance, but can only classify normal and abnormal classes. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/11431281170664160-1687854527320_Q64/Taotao-Liu-7.jpg)
... Lastly, we have a wave caused by the repolarization of the ventricles (T wave) [6]. Regarding ECG, in the literature, we can find many proposals focused on biometrics identification [7][8][9]. Rathore classified the proposals into two categories: handcrafted and non-handcrafted approaches. Within the former, we can distinguish between fiducials and non-fiducials solutions. ...
... With the increase of social pressure, people often maintain physical health through exercise, and arrhythmias during exercise can lead to physical discomfort, and serious ones can also lead to sudden death. Electrocardiogram (ECG) is able to reflect the condition of the heart by recording periodic changes in the heartbeat (Fan et al., 2021). Therefore, arrhythmias can be diagnosed by electrocardiogram. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/743492066213892-1554273730845_Q64/Zhang-Gong-2.jpg)
... The authors used different feature extraction methods, classifiers, and datasets. The authors of [23] proposed a two-level fusion PCANet (Principal Component Analysis Network) deep recognition network. It achieved an over 95% recognition rate. ...
... A sequential technique for identifying lung cancer in CT lung images was devised by Guo et al. [22]. The used approach included the utilization of both a CNN-based classifier and a feature-based classifier. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/715823069020161-1547676927374_Q64/Navid-Razmjooy.jpg)
... In their research, Yang et al. introduced a hybrid model named THC-Net for automated ECG classification. This model incorporates various techniques, including a Canonical Correlation Analysis (CCA) based on Principal Component Analysis (PCA) Convolutional Network, an Independent Component Analysis (ICA)-PCA Convolutional Network, and a Dempster-Shafer (D-S) theory-based Linear Support Vector Machine (SVM) [8]. By combining these techniques, THC-Net streamlines the ECG classification process and eliminates the need for manual feature construction based on specialized knowledge. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/616308701941761-1523950853199_Q64/Di-Wang-41.jpg)
... Such as in the NLSN [51], they proposed a Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern module to generate non-local attention with spherical locality sensitive hashing (LSH). Furthermore, in MHNAN [52], nonlocal information is extracted by a Mixed High-Order Attention (MHA) module. In another work, COLA-Net [31] tried to build a learnable non-local module to extract long-range dependencies within the degraded image. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/534345891762176-1504409396522_Q64/Jiang-Saibiao.jpg)
